Methods and compositions for diagnosis or prognosis of cardiovascular disease

ABSTRACT

The invention provides methods of screening a mammalian subject to determine if the subject is at risk to develop or is suffering from, cardiovascular disease. In one embodiment, the method comprises detecting a measurable feature of at least two biomarkers in an HDL subfraction, or in a complex containing apoA-I or apoA-III isolated from a biological sample obtained from the subject, wherein the at least two biomarkers are selected from the group consisting of apoA-I, apoA-II, apoB-100, Lp(a), apoC-I, and apoC-III, combinations or portions and/or derivatives thereof, and comparing the measurable features of the at least two biomarkers from the biological sample to a reference standard, wherein a difference in the measurable features of the at least two biomarkers from the biological sample and the reference standard is indicative of the presence or risk of cardiovascular disease in the subject.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 12/499,711,filed idly 8, 2009 now U.S. Pat. No. 8,241,861, which claims the benefitof U.S. Provisional Application No. 61/079,088, filed Jul. 8, 2008, bothwhich are expressly incorporated herein by reference in their entirety.

STATEMENT OF GOVERNMENT LICENSE RIGHTS

This invention was made with U.S. Government support under NIH grantnumber HL086798, awarded by the National Institutes of Health. The U.S.Government has certain rights in this invention.

STATEMENT REGARDING SEQUENCE LISTING

The sequence listing associated with this application is provided intext format in lieu of a paper copy and is hereby incorporated byreference into the specification. The name of the text file containingthe sequence listing is 39406_Seq_Final 2012-07-05.txt. The text file is75 KB; was created on Jul. 5, 2012; and is being submitted via EFS-Webwith the fling of the specification.

FIELD OF THE INVENTION

The present invention generally relates to methods, reagents, and kitsfor diagnosing cardiovascular disease in a subject, and particularlyrelates to the use of lipoprotein-associated markers to diagnosecardiovascular disease in a subject.

BACKGROUND

Cardiovascular disease is a leading cause of morbidity and mortality,particularly in developed areas such as the United States and WesternEuropean countries. The incidence of mortality from cardiovasculardisease has significantly decreased in the United States over the past30 years (see Braunwald, E., N. Engl. J. Med. 337:1360-1369, 1997;Hoyert, D. L., et al, “Deaths; Preliminary Data for 2003” in NationalVital Statistics Reports. Hyattsville: National Center for HealthStatistics, 2005). Many factors have contributed to this improvement inpatient outcome, including the identification of cardiovascular riskfactors, the application of medical technologies to treat acute coronarysyndrome, and the development of interventions that reducecardiovascular risk factors. Despite these advances, however,cardiovascular disease remains a leading cause of morbidity andmortality in developed countries (see Hoyert D. L., et al., NationalVital Statistics Reports, 2005).

Thus, there is a pressing need to identify markers that may be used forthe rapid, accurate and non-invasive diagnosis and/or assessment of therisk of cardiovascular disease, and also to assess the efficacy ofinterventions designed to slow the initiation and progress of thisdisorder.

SUMMARY

In accordance with the foregoing, in one aspect, the present inventionprovides a method of screening a mammalian subject to determine if thesubject is at risk to develop, or is suffering from, cardiovasculardisease, the method comprising detecting a measurable feature of atleast two biomarkers in an HDL subfraction, or in a complex containingapoA-I or apoA-II isolated from a biological sample obtained from thesubject, wherein the at least two biomarkers are selected from the groupconsisting of apoA-I, apoA-II, apoB-100, Lp(a), apoC-I, and apoC-III,combinations or portions and/or derivatives thereof, and comparing themeasurable features of the at least two biomarkers from the biologicalsample to a reference standard, wherein a difference in the measurablefeatures of the at least two biomarkers from the biological sample andthe reference standard is indicative of the presence or risk ofcardiovascular disease in the subject.

In another aspect, the present invention provides a method fordiagnosing and/or assessing the risk of CAD in a subject, comprisingdetermining changes in a biomarker profile comprising the relativeabundance of at least one, two, three, four, five, ten or morebiomarkers in an HDL subfraction or in a complex containing apoA-I orapoA-II isolated from a biological sample obtained from a test subjectas compared to the predetermined abundance of the at least one, two,three, four, five, ten or more biomarkers from a reference population ofapparently healthy subjects, wherein the biomarkers are selected fromthe biomarkers set forth in TABLE 3, TABLE 4, and TABLE 5.

In another aspect, the present invention provides a method of screeninga mammalian subject to determine if a test subject is at risk todevelop, is suffering from, or recovering from, cardiovascular disease,the method comprising detecting an alteration in the conformationalstructure of apoA-I present in the HDL subfraction or in a complexcontaining apoA-I or apoA-II isolated from a biological sample obtainedfrom the test subject in comparison to a reference standard, wherein adifference in the conformation of the apoA-I between the biologicalsample from the test subject and the reference standard is indicative ofthe presence or risk of cardiovascular disease in the subject.

In another aspect, the present invention provides a method fordetermining the efficacy of a treatment regimen for treating and/orpreventing cardiovascular disease in a subject by monitoring ameasurable feature of at least two biomarkers selected from the groupconsisting of apoA-I, apoA-II, apoB-100, Lp(a), apoC-I, and apoC-III,combinations or portions and/or derivatives thereof in an HDLsubfraction or in a complex containing apoA-I or apoA-II isolated from abiological sample obtained from the subject during treatment forcardiovascular disease.

In yet another aspect, the present invention provides a kit fordetermining susceptibility or presence of cardiovascular disease in amammalian subject based on the detection of at least one measurablefeature of at least one biomarker in a biological sample, an HDLsubfraction thereof, or a complex containing apoA-I or apoA-II isolatedfrom the biological sample, the kit comprising (i) one or more detectionreagents for detecting the at least one measurable feature of the atleast one biomarker selected from the group consisting of apoA-I,apoA-II, apoB-100, Lp(a), apoC-I, and apoC-III, and (ii) written indiciaindicating a positive correlation between the presence of the detectedfeature of the biomarker and the diagnosis or risk of developingcardiovascular disease.

DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of thisinvention will become more readily appreciated as the same become betterunderstood by reference to the following detailed description, whentaken in conjunction with the accompanying drawings, wherein:

FIG. 1 presents graphical results demonstrating the receiver operatingcharacteristic (ROC) curve of the prediction of cardiovascular disease(CAD) status based on random permutation analysis, as described inExample 2;

FIG. 2 graphically illustrates the prediction of CAD status by theproteomics CAD risk score “ProtCAD risk score” using a partial leastsquares discriminate analysis (PLS-DA) model built using a calibrationgroup (as described in Example 2). Using a sensitivity of 80%, theProtCAD risk score of each subject in the validation group at eachpermutation was used to predict their CAD status, as described inExample 2;

FIG. 3 graphically illustrates the power of the ProtCAD risk score todiscriminate between the CAD samples and healthy control samples basedon leave-one-out analysis. The ProtCAD risk score was derived fromPLS-DA analysis of MALDI-TOF-MS mass spectra of HDL tryptic digests,using a leave-one-out experiment for all 18 CAD and 20 control subjects,as described in Example 2;

FIG. 4 graphically illustrates the PLS-DA regression vectors (y-axis) ofthe leave-one-out PLS-DA model that distinguish CAD and controlsubjects. The x-axis (m/z) represents mass channels of the MALDI-TOFmass spectrum. Positive and negative features on the regression vectorindicate an increase and decrease, respectively, of the signals from CADsamples relative to control samples, as described in Example 3;

FIG. 5A graphically illustrates the strong positive feature in thePLS-DA regression vector at m/z 1440.68 identified by LC-MALDI-TOF/TOFMS/MS as corresponding to peptides derived from Lp(a), as described inExample 3;

FIG. 5B graphically illustrates the strong positive feature in thePLS-DA regression vector at Ink 1904.91 identified by LC-MALDI-TOF/TOFMS/MS as corresponding to peptides derived from Lp(a), as described inExample 3;

FIG. 5C illustrates the results of the MASCOT database search of theMS/MS spectrum of the peptide of m/z 1440.68 that identified Lp(a) witha high confidence level (CI100%), as described in Example 3;

FIG. 6A to FIG. 6D graphically illustrate the PLS-DA regression vectorfeatures corresponding to apoA-I peptides containing Met112, with pairsof specific informative features at m/z 1411 and 1427 and m/z 2.645 and2661 corresponding to signals detected for M and M+16 respectively,wherein the positive features signify an increase of oxidized form ofMet112 peptide, and negative features at ink 1411 and m/z 2645 indicatea decreased level of the peptide containing unoxidized Met1.12 in theCAD samples, as described in Example 4;

FIG. 7 graphically illustrates the differential digestion efficiency inCAD HDL as compared to normal HDL, in which multiple features in theregression vector (y-axis) correspond to peptides derived from apoA-I(x-axis) with differential features at the N-terminal (residues 46-59,6077) and the C-terminal (residues 207-215) domains, indicating aconformational change in apoA-I in the HDL of CAD subjects, as describedin Example 5;

FIG. 8 graphically illustrates the results of principle componentanalysis (PCA) of the average mass spectra from HDL₂ isolated from 3control and 3 CAD subjects mixed in protein ratios (w/w) of 1:0, 1:3,1:1, 3:1, and 0:1, digested with trypsin, and subjected to MALDI-TOF-MS(for simplicity only two pairs are shown in FIG. 8), as described inExample 1;

FIG. 9A graphically illustrates the reproducibility of the MALDI-TOFspectra with selected mass channels of Met112 peptides represented onthe plot as median normalized intensities, as described in Example 1;

FIG. 9B graphically illustrates the reproducibility of the MALDI-TOFspectra of multiple spots of samples with selected mass channels ofMet112 peptides represented on the plot as median normalizedintensities, as described in Example 1;

FIG. 9C graphically illustrates the reproducibility of a series oftrypsin digestions carried out on the same day followed by MALDI-TOFspectra with selected mass channels of Met112 peptides represented onthe plot as median normalized intensities, as described in Example 1;

FIG. 9D graphically illustrates the reproducibility of a series oftrypsin digestions carried out on different days followed by MALDI-TOFspectra with selected mass channels of Met112 peptides represented onthe plot as median normalized intensities, as described in Example 1;

FIG. 10A graphically illustrates a receiver operating characteristic(ROC) curve constructed using a ProtCAD score based on a PLS-DA modelbuilt from a leave-one-out approach, demonstrating high selectivity(true positive rate=y axis) and high specificity (false positive rate=xaxis), as described in Example 2; and

FIG. 10B graphically illustrates the odds ratio of the ProCAD score as afunction of the false positive rate, demonstrating that at an 80% levelof specificity (corresponding to a 90% sensitivity level as shown inFIG. 10A), the odds ratio was approximately 35, as described in Example2.

DETAILED DESCRIPTION

As used herein, the term “cardiovascular disease” or “CAD,” generallyrefers to heart and blood vessel diseases, including atherosclerosis,coronary heart disease, cerebrovascular disease, and peripheral vasculardisease. Cardiovascular disorders are acute manifestations of CAD andinclude myocardial infarction, stroke, angina pectoris, transientischemic attacks, and congestive heart failure. Cardiovascular disease,including atherosclerosis, usually results from the build up of fattymaterial, inflammatory cells, extracellular matrix, and plaque. Clinicalsymptoms and signs indicating the presence of CAD include one or more ofthe following: chest pain and other forms of angina, shortness ofbreath, sweatiness, Q waves or inverted T waves on an EKG, a highcalcium score by CT scan, at least one stenotic lesion on coronaryangiography, or heart attack documented by Changes in myocardial enzymelevels (e.g., troponin, CK levels).

As used herein, the term “biomarker” is a biological compound, such as aprotein or a peptide fragment thereof, including a polypeptide orpeptide that may be isolated from or measured in the biological sample,wherein the biomarker is differentially present or absent, or present ina different structure (i.e., post-translationally modified, or in analtered structural conformation) in a sample taken from a subject havingestablished or potentially clinically significant CAD as compared to acomparable sample taken from an apparently normal subject that does nothave CAD. A biomarker can be an intact molecule, or it can be a portionthereof or an altered structure thereof; that may be partiallyfunctional and recognized, for example, by a specific binding protein orother detection method. A biomarker is considered to be informative forCAD if a measurable feature of the biomarker is associated with thepresence of CAD in a subject in comparison to a predetermined value or areference profile from a control population. Such a measurable featuremay include, for example, the presence, absence, or concentration of thebiomarker, or a portion thereof, in the biological sample, an alteredstructure, such as, for example, the presence or amount of apost-translational modification, such as oxidation at one or morepositions on the amino acid sequence of the biomarker or, for example,the presence of an altered conformation in comparison to theconformation of the biomarker in normal control subjects, and/or thepresence, amount, or altered structure of the biomarker as a part of aprofile of more than one biomarker. A measurable aspect of a biomarkeris also referred to as a feature. A feature may be a ratio of two ormore measurable aspects of biomarkers. A biomarker profile comprises atleast two measurable informative features, and may comprise at leastthree, four, five, 10, 20, 30 or more informative features. Thebiomarker profile may also comprise at least one measurable aspect of atleast one feature relative to at least one internal standard.

As used herein, the term “predetermined value” refers to the amountand/or structure of one or more biomarkers in biological samplesobtained from the general population or from a select population ofsubjects. For example, the select population may be comprised ofapparently healthy subjects, such as individuals who have not previouslyhad any sign or symptoms indicating the presence of CAD. In anotherexample, the predetermined value may be comprised of subjects havingestablished CAD. The predetermined value can be a cut-off value or arange. The predetermined value can be established based upon comparativemeasurements between apparently healthy subjects and subjects withestablished CAD, as described herein.

As used herein, the term “high density lipoprotein” or “HDL, or asubfraction thereof” includes protein or lipoprotein complexes with adensity from about 1.06 to about 1.21 g/mL, or from about 1.06 to 1.10g/mL, or from about 1.10 to about 1.21 g/mL, or a complex containingapoA-I or apoA-II. HDL may be prepared by density ultracentrifugation,as described in Mendez, A. J., et al., J. Biol Chem. 266:10104-10111,1991, from plasma, serum, bodily fluids, or tissue. The HDL₃ subfractionin the density range of about 1.110 to about 1.210 g/mL, and the HDL₂subfraction in the density range of about 1.06 to about 1.125 g/mL maybe isolated from plasma, serum, bodily fluids, tissue or total HDL bysequential density ultracentrifugation, as described in Mendez, supra.HDL is known to contain two major proteins, apolipoprotein A-I (apoA-I)and apolipoprotein A-II (apoA-II); therefore, in some embodiments, theterm “HDL, or a subfraction thereof” also includes an apoA-I and/or anapoA-II containing protein or lipoprotein complex which may be isolated,for example, by immunoaffinity with anti-apoA-I or anti-apoA-IIantibodies.

As used herein, the term “HDL-associated” refers to any biologicalcompounds that float in the density range of HDL (d=about 1.06 to about1.21 g/mL) and/or molecules present in a complex containing apoA-Iand/or apoA-II, including full-length proteins and fragments thereof,including peptides or lipid-protein complexes, such as microparticles,in HDL isolated from any sample, including lesions, blood, urine,cerebral spinal fluid, bronchoalveolar fluid, joint fluid, or tissue orfluid samples.

As used herein, the term “HDL₂-associated” refers to any biologicalcompounds that float in the density range of HDL₂ (d=about 1.06 to about1.125 g/mL) and/or molecules present in a complex containing apoA-Iand/or apoA-II, including full-length proteins, and fragments thereof,including peptides, or lipid-protein complexes such as microparticles,in HDL isolated from any sample, including lesions, blood, urine,cerebral spinal fluid, bronchoalveolar fluid, joint fluid, or tissue orfluid samples.

As used herein, the term “mass spectrometer” refers to a device able tovolatilize/ionize analytes to form gas-phase ions and determine theirabsolute or relative molecular masses. Suitable forms ofvolatilization/ionization are matrix-assisted laser desorptionionization (MALDI), electrospray, laser/light, thermal, electrical,atomized/sprayed and the like, or combinations thereof. Suitable formsof mass spectrometry include, but are not limited to, ion trapinstruments, quadrupole instruments, electrostatic and magnetic sectorinstruments, time of flight instruments, time of flight tandem massspectrometer (TOF MS/MS), Fourier-transform mass spectrometers, andhybrid instruments composed of various combinations of these types ofmass analyzers. These instruments may, in turn, be interfaced with avariety of sources that fractionate the samples (for example, liquidchromatography or solid-phase adsorption techniques based on chemical,or biological properties) and that ionize the samples for introductioninto the mass spectrometer, including matrix-assisted laser desorption(MALDI), electrospray, or nanospray ionization (ESI) or combinationsthereof.

As used herein, the term “affinity detection” or “affinity purified”refers to any method that selectively detects and/or enriches theprotein or analyte of interest. This includes methods based on physicalproperties like charge, amino acid sequence, and hydrophobicity, and caninvolve many different compounds that have an affinity for the analyteof interest, including, but not limited to, antibodies, resins, RNA,DNA, proteins, hydrophobic materials, charged materials, and dyes.

As used herein, the term “antibody” encompasses antibodies and antibodyfragments thereof derived from any antibody-producing mammal (e.g.,mouse, rat, rabbit, and primate including human) that specifically bindto the biomarkers or portions thereof. Exemplary antibodies includepolyclonal, monoclonal, and recombinant antibodies; multispecificantibodies (e.g., bispecific antibodies); humanized antibodies; murineantibodies; chimeric, mouse-human, mouse-primate, primate-humanmonoclonal antibodies; and anti-idiotype antibodies, and may be anyintact molecule or fragment thereof.

As used herein, the term “antibody fragment” refers to a portion derivedfrom or related to a full length anti-biomarker antibody, generallyincluding the antigen binding or variable region thereof. Illustrativeexamples of antibody fragments include Fab, Fab′, F(ab)₂, F(ab′)₂ and Fvfragments, scFv fragments, diabodies, linear antibodies, single-chainantibody molecules and multispecific antibodies formed from antibodyfragments. Antibody and antibody fragments as used here may beincorporated into other proteins that can be produced by a variety ofsystems, including, but not limited to, bacteria, viruses, yeast, andmammalian cells.

As used herein, “a subject” includes all mammals, including withoutlimitation humans, non-human primates, dogs, cats, horses, sheep, goats,cows, rabbits, pigs and rodents.

As used herein, the term “percent identity” or “percent identical,” whenused in connection with a biomarker used in the practice of the presentinvention, is defined as the percentage of amino acid residues in abiomarker sequence that are identical with the amino acid sequence of aspecified biomarker after aligning the sequences to achieve the maximumpercent identity. When making the comparison, no gaps are introducedinto the biomarker sequences in order to achieve the best alignment.

Amino acid sequence identity can be determined, for example, in thefollowing manner. The amino acid sequence of a biomarker is used tosearch a protein sequence database, such as the GenBank database, usingthe BLASTP program. The program is used in the ungapped mode. Defaultfiltering is used to remove sequence homologies due to regions of lowcomplexity. The default parameters of BLASTP are utilized.

As used herein, the term “derivatives” of a biomarker, includingproteins and peptide fragments thereof, include an insertion, deletion,or substitution mutant. Preferably, any substitution mutation isconservative in that it minimally disrupts the biochemical properties ofthe biomarker. Thus, where mutations are introduced to substitute aminoacid residues, positively-charged residues (H, K, and R) preferably aresubstituted with positively-charged residues; negatively-chargedresidues (D and E) are preferably substituted with negatively-chargedresidues; neutral polar residues (C, G, N, Q, S, T, and Y) arepreferably substituted with neutral polar residues; and neutralnon-polar residues (A, F, I, L, M, P, V, and W) are preferablysubstituted with neutral non-polar residues.

As used herein, the amino acid residues are abbreviated as follows:alanine (Ala; A), asparagine (Asn; N), aspartic acid (Asp; D), arginine(Arg; R), cysteine (Cys; C), glutamic acid (Glu; E), glutamine (Gln; Q),glycine (Gly; G), histidine (His; H), isoleucine (Ile; I), leucine (Leu;L), lysine (Lys; K), methionine (Met; M), phenylalanine (Phe; F),proline (Pro; P), serine (Ser; S), threonine (Thr; T), tryptophan (Trp;W), tyrosine (Tyr; Y), and valine (Val; V).

In the broadest sense, the naturally occurring amino acids can bedivided into groups based upon the chemical characteristic of the sidechain of the respective amino acids. By “hydrophobic” amino acid ismeant either Ile, Leu, Met, Phe, Trp, Tyr, Val, Ala, Cys, or Pro. By“hydrophilic” amino acid is meant either Gly, Asn, Gln, Ser, Thr, Asp,Glu, Lys, Arg, or His. This grouping of amino acids can be furthersubclassed as follows. By “uncharged hydrophilic” amino acid is meanteither Ser, Thr, Asn, or Gln. By “acidic” amino acid is meant either Gluor Asp. By “basic” amino acid is meant either Lys, Arg, or His.

In the past, studies have been done to identify proteins in the blood ofa subject that could be used as markers for cardiovascular disease (see,e.g., Stanley et al., Dis. Markers 20:167-178, 2004). However, thisapproach has been hampered by the vast number of candidate proteins inblood plasma in concentrations that vary over six orders of magnitude,which complicate the discovery and validation processes (Qian, W. J., etal., Proteomics 5:572-584, 2005). Cholesterol is present in the blood asfree and esterified cholesterol within lipoprotein particles, commonlyknown as chylomicrons, very low density lipoproteins (VLDLs), lowdensity lipoproteins (LDLs), and high density lipoproteins (HDLs). HDLparticles vary in size and density due to the differences in the numberof apolipoproteins on the surface of the particles and the amount ofcholesterol esters in the core of HDL (see Asztalos, B. F., et al., Am.J. Cardiol. 91(7):12E-17E, 2003). HDL is composed of two principalsubfractions based on density: HDL₂ and the denser HDL₃.

Elevated LDL cholesterol and total cholesterol are directly related toan increased risk of cardiovascular disease. See Anderson et al.,“Cholesterol and Mortality: 30 years of Follow Up from the FraminghamStudy,” JAMA 257:2176-90, 1987. In contrast, it has been establishedthat the risk of cardiovascular disease is inversely proportional toplasma levels of HDL and the major HDL apolipoprotein, apoA-I (Gordon,D. J., et al., N. Engl. J. Med. 321:1311-1316, 1989). Studies have shownthat high HDL levels are associated with longevity (Barzilai, N., etal., JAMA 290:2030-2040, 2003). Consistent with these findings, anabnormally low HDL level is a well-accepted risk factor for thedevelopment of clinically significant atherosclerosis (particularlycommon in men with premature atherosclerosis (Gordon, D. J., et al., N.Engl. J. Med. 321:1311-1316, 1989; Wilson, P. W., et al.,Arteriosclerosis 8:737-741, 1988)). The mechanism by which HDL rendersits protective effect against atherosclerosis is the subject ofcontinued debate. Some studies have implicated that HDL may directlyprotect against atherosclerosis by removing cholesterol from artery wallmacrophages (see Tall, A. R., et al., J. Clin. Invest. 110:899-904,2002; Oram, J. F., et al., Arterioscler. Thromb. Vasc. Biol. 23:720-727,2003). Other studies have reported that HDL protects against LDLoxidative modification, which is believed to be central to theinitiation and progression of atherosclerosis (see, e.g., Parthasarathy,S., et al., Biochim. Biophys. Acta 1044:275-283, 1990; Barter, P. J., etal., Circ Res 95:764-772, 2004). However, while HDL/LDL ratios have beencorrelated with risk for cardiovascular disease on an overallpopulation, HDL and/or LDL measurements have not been reliableindicators of risk at an individual level.

Animal studies indicate that one important mechanism by which HDLprotects against development of atherosclerosis involves reversecholesterol transport in which HDL accepts cholesterol from macrophagefoam cells in the artery wall and transports it back to the liver forexcretion. HDL's cardioprotective effects may also depend on itsanti-inflammatory properties. Indeed, HDL contains multiple acute phaseresponse proteins, protease inhibitors and complement regulatoryproteins (Vaisar, T., et al., J. Clin. Invest. 117(3):746-756 (2007).Although HDL-cholesterol (HDL-C) levels are widely used to assess therisk for CAD, studies with genetically engineered animals convincinglydemonstrate that changes in HDL metabolism can promote atherosclerosisby pathways that are independent of plasma levels of HDL-C. Also, thefailure of recent clinical trials of a therapy that elevates HDL-Clevels suggests that HDL can become dysfunctional in humans.

In accordance with the foregoing, in one aspect, a method of screening amammalian test subject to determine if the subject is at risk todevelop, or is suffering from, cardiovascular disease. The methodcomprises detecting a measurable feature of at least two biomarkerspresent in an HDL subfraction, or in a complex containing apoA-I orapoA-II isolated from a biological sample obtained from the subject. Themeasurable features of the at least two biomarkers selected from thegroup consisting of apoA-I, apoA-II, apoB-100, Lp(a), apoC-I, andapoC-III, combinations or portions and/or derivatives thereof, are thencompared to a reference standard that is derived from measurements ofthe corresponding biomarkers present in comparable HDL subfractions orcomplexes isolated from biological samples obtained from a controlpopulation, such as a population of apparently healthy subjects. Adifference in the measurable features of the at least two biomarkersbetween the test subject's sample and the reference standard, such as anaverage value from the control population, is indicative of the presenceor risk of developing CAD in the subject. In some embodiments, themethod further comprises determining whether the subject is exhibitingsymptoms related to CAD.

The methods of this aspect of the invention are useful to screen anymammalian subject, including humans, non-human primates, canines,felines, murines, bovines, equines, and porcines. A human subject may beapparently healthy or may be diagnosed as having a low HDL:LDL ratioand/or as being at risk for CAD based on certain known risk factors suchas high blood pressure, high cholesterol, obesity, or geneticpredisposition for CAD. The methods described herein are especiallyuseful to identify subjects that are at high risk of developing CAD inorder to determine what type of therapy is most suitable and to avoidpotential side effects due to the use of medications in low risksubjects. For example, prophylactic therapy is useful for subjects atsome risk for CAD, including a low fat diet and exercise. For those athigher risk, a number of drugs may be prescribed by physicians, such aslipid-lowering medications as well as medications to lower bloodpressure in hypertensive patients. For subjects at high risk, moreaggressive therapy may be indicated, such as administration of multiplemedications.

In order to conduct sample analysis, a biological sample containing HDLis provided to be screened, including, but not limited to, whole bloodor blood fractions (e.g., serum), bodily fluid, urine, cultured cells,tissue biopsies, or other tissue preparations. In some embodiments ofthe method of the invention, the biological samples include total HDL(density=about 1.06 to about 1.21 g/mL) or protein complexes that areisolated in this density range. In some embodiments of the method, acomplex containing apoA-I and/or apoA-II is isolated from the biologicalsample. In other embodiments of the method of the invention, an HDL₂subfraction (density=about 1.06 to about 1.125 g/mL) is isolated fromthe biological sample prior to analysis. The HDL₂ fraction may beisolated using any suitable method, such as, for example, through theuse of ultracentrifugation, as described in Example 1.

In some embodiments, one or more of the biomarkers apoA-I, apoA-II,apoB-100, Lp(a), apoC-I, and apoC-III, including apoA-I oxidized atmethionine residues and/or other HDL-associated peptides and/or proteinsare isolated by liquid chromatography, affinity chromatography, orantibody-based methods from biological samples such as, but not limitedto, blood, plasma, serum, urine, tissue, or atherosclerotic lesions.

As described in Examples 1-5, the present inventors have usedmatrix-assisted laser desorption mass spectrometry (MALDI-MS) toinvestigate the HDL proteome through the use of tryptic digestion. Itwas determined that the use of pattern recognition with two powerfullinear algebraic techniques principal component analysis (PCA) andpartial least squares discriminate analysis (PLS-DA) could distinguishbetween tryptic digested HDL₂ subfractions generated from control andCAD subjects at a high level of specificity and selectivity, asdescribed in Example 2. Tandem mass spectrometry of informative massfeatures used to distinguish between normal and CAD subjects revealed aset of biomarkers for CAD as shown in TABLE 2 which include apoA-I (SEQID NO:1), apoA-II (SEQ ID NO:2), apoB-100 (SEQ ID NO:3), Lp(a) (SEQ IDNO:4), apoC-I (SEQ ID NO:5), apoC-III (SEQ ID NO:6), SAA4 (SEQ ID NO:7)and ApoE (SEQ ID NO:8), and peptide fragments and measurable featuresthereof.

The informative features that were identified that are useful todistinguish between normal and CAD subjects fall into the followingclasses: (1) increased levels of particular peptides/proteins in CADsubjects as compared to normal controls, for example, peptides derivedfrom Lp(a) and/or apoC-III as shown in TABLE 3 and TABLE 4; (2)decreased levels of particular peptides/proteins in CAD subjects ascompared to normal controls, for example, peptides derived from apoC-4as shown in TABLE 3 and TABLE 5; (3) post-translational modifications ofparticular peptides/proteins in CAD subjects as compared to normalcontrols, for example, oxidation of M112 in apoA-I as shown in TABLE 3and TABLE 6; and (4) altered conformational structure of particularpeptides/proteins in CAD subjects as compared to normal controls, forexample, apoA-I as shown in FIG. 7 and described in Example 5.

These results demonstrate that HDL isolated from subjects with CAD isselectively enriched in oxidized amino acids and certain proteins, andthat the distinct cargo carried by the lipoprotein in subjects withclinically significant CAD may be assessed in a mammalian subject todetermine his or her risk for developing CAD, the presence of CAD,and/or the efficacy of treatment of the subject for CAD. Therefore, theidentification of peptides/proteins that are present in HDL of subjectssuffering from CAD in amounts or structures that differ from normalsubjects provide new biomarkers which are useful in assays that areprognostic and/or diagnostic for the presence of CAD and relateddisorders. The biomarkers may also be used in various assays to assessthe effects of exogenous compounds for the treatment of CAD.

In one embodiment of this aspect of the invention, at least one of themeasurable features indicative of the presence or risk of cardiovasculardisease comprises an increased amount of at least one of the biomarkersin the HDL subfraction of the biological sample selected from the groupconsisting of apoA-I, apoB-100, apoC-III, and Lp(a), or portions and/orderivatives thereof, in comparison to the reference standard. Forexample, as demonstrated in Example 3, TABLE 3, and TABLE 4, trypticpeptides have been identified from apoA-I, apoB-400, apoC-III, and Lp(a)that were increased in HDL₂ of CAD subjects as compared to normalcontrol subjects. As shown in Examples 1 and 2, these peptides withincreased frequency in CAD subjects are informative features for theprognosis and/or diagnosis of CAD.

In another embodiment of this aspect of the invention, at least one ofthe measurable features indicative of the presence or risk ofcardiovascular disease comprises a decreased amount of at least one ofthe biomarkers in the HDL subfraction of the biological sample selectedfrom the group consisting of apoA-I and apoC-I, or portions and/orderivatives thereof, in comparison to the reference standard. Forexample, as demonstrated in Example 3, TABLE 3, and TABLE 5, trypticpeptides have been identified from apoA-I and apoC-I that were decreasedin HDL₂ of CAD subjects as compared to normal control subjects. As shownin Examples 1 and 2, these peptides with decreased frequency in CADsubjects are informative features for the prognosis and/or diagnosis ofCAD.

In another embodiment of the invention, at least one of the measurablefeatures indicative of the presence or risk of cardiovascular diseasecomprises a post-translational modification of a peptide derived fromapoA-I in the HDL subfraction of the biological sample, in comparison tothe reference standard. For example, as demonstrated in Example 4 andTABLE 6, it has been determined that the oxidation state of apoA-I atM112 is indicative of the presence of CAD.

In the practice of the methods of the methods of this aspect of theinvention, a measurable feature of at least two biomarkers (such as atleast 3, at least 4, at least 5, or at least 6) selected from the groupconsisting of apoA-1, apoA-II, apoB-100, Lp(a), apoC-I, and apoC-III isdetected, in accordance with this aspect of the invention, proteinshaving at least 90% identity (such as at least 95% identical, or atleast 98% identical) with apoA-I (SEQ ID NO:1), apoA-II (SEQ ID NO:2),apoB-100 (SEQ ID NO:3), Lp(a) (SEQ ID NO:4), apoC-I (SEQ ID NO:5), andapoC-III (SEQ ID NO:6), and peptides derived therefrom, may be used asbiomarkers for CAD, which are present at a differential level in CADsubjects as compared to normal control subjects. Peptide fragmentsderived from SEQ ID NOS: 1, 2, 3, 4, 5, and 6 may also be used asbiomarkers, such as peptides from about 4 amino acids to at least about20 amino acids or more. Representative peptide fragments that may beused as biomarkers in which an increased amount of the biomarker in HDL₂is indicative of the presence or risk of CAD include the peptides withpositive regression vector values shown in TABLE 3 and TABLE 4.Representative peptide fragments that may be used as biomarkers in whicha decreased amount of the biomarker in HDL₂ is indicative of thepresence or risk of CAD include the peptides with negative regressionvector values shown in TABLE 3 and TABLE 5.

The presence and/or amount of the two or more HDL-associated biomarkersin a biological sample comprising total HDL, or a subfraction thereof,may be determined using any suitable assay capable of detecting theamount of the one or more biomarkers. Such assay methods include, butare not limited to, mass spectrometry, liquid chromatography, thin layerchromatography, fluorometry, radioisotope detection, affinity detection,and antibody detection. Other detection paradigms may optionally beused, such as optical methods, electrochemical methods, atomic forcemicroscopy, and radio frequency methods (e.g., multipolar resonancespectroscopy). Optical methods include, for example, microscopy,detection of fluorescence, luminescence, chemiluminescence, absorbance,reflectance, and transmittance.

In one embodiment, the presence and amount of one or more HDL-associatedbiomarkers is determined by mass spectrometry. In accordance with thisembodiment, biological samples may be obtained and used directly, or maybe separated into total HDL or an HDL₂ subfraction. The HDL-associatedproteins are digested into peptides with any suitable enzyme such astrypsin, which cleaves adjacent to lysine (K) or arginine (R) residuesin proteins. The peptides are then analyzed by a mass spectrometrymethod such as MALDI-TOF-MS or M/MS (solid phase), liquid chromatography(LC)-MS or MS/MS, μLC-ESI-MS/MS, and iTRAQ,™ ICAT, or other forms ofisotope tagging. Any suitable method may be used for differentialisotope labeling of proteins and/or peptide, such as the use of acompound or isotope-labeled compound that reacts with an amino acidfunctional group. Label-specific fragment ions allow one to quantify thedifferences in relative abundance between samples. For example, oneuseful approach to achieve quantitative results is the use of MALDITOF/TOF or QTOF mass spectrometers and iTRAQ™, a commercially availablestable isotope labeling system (Applied Biosystems, Foster City,Calif.). The iTRAQ™ labeling system allows selective labeling of up tofour different samples which are distinguished from one another in themixture by MS/MS analysis.

By way of representative example, the method of MALDI-TOF-MS/MS involvesthe following steps. The samples are prepared and separated with fluidicdevices, such as microfluidic devices, and spotted on a MALDI plate forlaser-desorption ionization. Mass spectra are taken every few seconds,followed by isolation of the most intense peptide ions, or the peptideions of interest (e.g., one derived from specific peptides),fragmentation by collisions with an inert gas, and recording of a massspectrum of the fragments. This fragment mass spectrum, known as MS/MSspectrum, tandem mass spectrum, or MS² spectrum, consists mainly of N-and C-terminal fragments of the peptide ions at the amide bonds, calledb ions and y ions, respectively. The spectra are then matched tosequence databases, as further described in Example 3.

In a typical application of MS analysis, proteins in a biological sampleare reduced, alkylated, digested into peptides with trypsin, andanalyzed using multidimensional liquid chromatography and tandem massspectrometry (MS/MS). Tryptic peptides are then subjected tomultidimensional chromatography in concert with MS/MS analysis. Inmultidimensional chromatography, the first chromatographic dimensiontypically involves separation of digested peptides on a strong cationexchange column. The peptides are then typically separated through areverse-phase column with increasing concentrations of acetonitrile andthen introduced into the source of the mass spectrometer or fractionateddirectly onto a MALDI sample plate. Tandem mass spectra may be acquiredin the data-dependent mode on an ion-trap, QTOF or MALDI-TOF/TOFinstrument. The most abundant peaks from a survey scan are submitted totandem MS analysis. In other applications, peaks that differ inintensity between samples of interest (e.g., a control population ofapparently healthy subjects and subjects with established CVD) areselected from the MS or MS/MS spectra by a suitable method such aspattern recognition, cluster analysis, or relative abundance (see Rocke,D. M., Semin. Cell Dev. Biol. 15:703-713, 2004; Ghazalpour, A., et al.,Lipid Res. 45; 1793-1805, 2004). The collection of tandem mass spectramay be submitted for a database search against a database (e.g., theHuman International Protein Index (IPI) database, using the SEQUESTsearch engine (see Kersey, P. J., et al., “The International ProteinIndex: An Integrated Database for Proteomics Experiments,” Proteomics4:1985-1988, 2004)), using software programs such as PeptideProphet(Nesvizhskii, A. I., et al., Anal. Chem. 75:4646-4658, 2003) andProteinProphet (Yan, W., et al., Mol. Cell Proteomics 3:1039-1041, 2004)in order to refine peptide and protein identification.

To achieve semiquantitative results, protein abundance is estimated bythe number of MS/MS spectra, the number of peptides detected, or by thepercent of the protein sequence covered in the analysis. Quantitativeresults can be obtained with ICAT isotope tagging, iTRAQ™ isotopelabeling, or other modifications or peptides involving stable isotopes.Label-specific ions or fragment ions allow quantification of differencesbetween samples based on their relative abundance.

Mass spectrometry detection methods may include the use ofisotope-labeled peptides or proteins. In accordance with one example ofthis detection method, as described by Zou, H., et al., Cell107:715-726, 2001, a tryptic peptide is chosen from a protein ofinterest. The tryptic peptide is then synthesized to incorporate one ormore stable isotope-labeled amino acids. The native peptide and thesynthetic-labeled peptide share physical properties including size,charge, hydrophobicity, ionic character, and amenability to ionization.When mixed, they elute together chromatographically, migrate togetherelectrophoretically, and ionize with the same intensity. However, theydiffer in molecular weight from as little as 1 to over 10 Daltons,depending on which stable isotope amino acid is chosen forincorporation. The native peptide and the synthetic peptide are easilydistinguishable by mass spectrometry. The synthetic peptide is used inan assay by adding a known amount of the synthetic peptide to abiological sample. In another example of this detection method, anisotope-labeled protein is prepared by a suitable method, such as byusing a bacterial expression system and growing the bacteria on mediumenriched with 15N-Nitrate or other isotope-labeled nutrients. Theisotope-labeled peptide or protein is added to the sample containingnative proteins and the mixture is then digested and analyzed by massspectrometry as described herein. Extracted ion chromatograms orselected ion chromatograms or peak ratios in a full scan mass spectrumare then generated for the native peptide and the synthetic peptide. Thequantity of the native peptide is then calculated using ratios of ioncurrent or peak ratios.

Another detection method that utilizes labeled peptide fragments isisotope-coded affinity tagging (ICAT). This technique, as described inGygi, S. P., et al., Nature Biotech. 17:994-999, 1999, involves the useof isotope tags that covalently bind to specific amino acids (cysteines)within a protein of interest. For example, the tag may contain threefunctional elements including a biotin tag (used during affinitycapture), an isotopically encoded linker chain (such as an ether linkagewith either eight hydrogens or eight deuteriums), and the reactivegroup, which binds to and modifies the cysteine residues of the protein.The isotope tag is used in an assay by labeling a control sample withthe light version of the tag and labeling a test sample with the heavyversion of the tag. The two samples are then combined, enzymaticallydigested, and the labeled cysteinyl residues may be captured usingavidin affinity chromatography. The captured peptides are then analyzedby mass spectrometry, which can determine the relative abundance foreach peptide-pair.

In another embodiment, antibodies are used in an immunoassay to detectone or more biomarkers in accordance with the method of this aspect ofthe invention. Such immunoassays may comprise an antibody to one or moreof the biomarkers. The antibody is mixed with a sample suspected ofcontaining the biomarker and monitored for biomarker-antibody binding.For example, the biomarker can be detected in an enzyme-linkedimmunosorbent assay (ELISA), in which a biomarker antibody is bound to asolid phase, such as a chip, and an enzyme-antibody conjugate is used todetect and/or quantify the biomarker(s) present in a sample.

In another aspect, the present invention provides a method of screeninga mammalian subject to determine if the subject is at risk to develop,or is suffering from, or is recovering from a cardiovascular disease,the method comprising detecting an alteration in the conformationalstructure of apoA-I present in the HDL subfraction of a biologicalsample obtained from the test subject in comparison to a referencestandard, wherein a difference in the conformation of the apoA-I betweenthe biological sample from the subject and the reference standard isindicative of the presence or risk of cardiovascular disease in thesubject.

In order to conduct sample analysis, a biological sample containing HDLis provided to be screened. Any HDL containing sample may be utilizedwith the methods described herein, including but not limited to wholeblood or blood fractions (e.g., serum), bodily fluid, urine, culturedcells, biopsies or other tissue preparations. In some embodiments, thebiological samples include total HDL (density=about 1.06 to about 1.21g/mL) or protein complexes that are isolated in this density range. Insome embodiments, an HDL₂ subfraction (density=about 1.06 to about 1.125g/mL) is isolated from the biological sample prior to analysis. In someembodiments, the HDL subfraction may be isolated by affinity isolationwith polyclonal antibodies against apoA-I, the major protein in HDL orwith polyclonal antibodies raised against other HDL associated proteins.

As described in Example 5, and shown in FIG. 7, it was determined thattwo tryptic peptides originating from N-terminal regions of apoA-I weresignificantly increased in the HDL subfraction of CAD subjects ascompared to normal controls, while one tryptic peptide originating fromthe C-terminal region of apoA-I was significantly decreased. Althoughthese N-terminal and C-terminal peptides are distant in the apoA-Isequence, when mapped to the double-belt model of the lipid-associatedHDL particle apoA-I (Davidson, W. S., et al., J. Biol. Chem.282(30:22249-22253, 2007, or the spherical HDL particle apoA-I model,the peptides displaying significant changes in CAD subjects were foundto be in close proximity, as discussed in Example 5.

The conformation of apoA-I may be determined using any suitable method,such as by digesting the HDL subfraction of the biological sample withtrypsin, followed by mass spectrometry analysis to measure the presenceand/or amount of the tryptic fragments of apoA-I as compared to areference standard, such as apoA-I isolated from normal controlsubjects. For example, the reference standard could be an exogenousisotopically labeled apoA-I which serves as an internal reference towhich the intensity of individual peptides derived from apoA-I from theHDL subfraction of the biological sample would be related by a firstratio (i.e., apoA-I peptide from biological test sample/apoA-I peptidefrom reference standard). This first ratio would then be compared to asecond ratio (i.e., apoA-I peptide from healthy control sample/apoA-Ipeptide from reference standard) to detect a difference in the amount ofapoA-I peptides in the tested sample relative to the expected ratio in ahealthy control sample, thereby indicating an altered apoA-Iconformation.

In another example, the conformation of apoA-I may be determined bycircular dichroism (CD), or with a monoclonal antibody that specificallydetects the altered conformation of apoA-I. Methods of generating anantibody specific to an altered conformation of apoA-I are well known inthe art, for example, see Marcel, Y. L., et al., “Lipid PeroxidationChanges the Expression of Specific Epitopes of Apolipoprotein A-I,” J.Biol. Chem. 264(33):19942-19950, Nov. 25, 1989; Milthorp, P., et al.,“Immunochemical characterization of apolipoprotein A-I from normal humanplasma. In vitro modification of apo A-I antigens,” Arteriosclerosis6(3):285-96, May-June 1986; Marcel, et al, “Monoclonal antibodies andthe characterization of apolipoprotein structure and function,” Prog.Lipid Res. 23(4):169-195, 1984; and Weech, P. K., et al.,“Apolipoprotein A-I from normal human plasma: definition of threedistinct antigenic determinants,” Biochim. Biophys. Acta 835(2):390-401,Jul. 9, 1985, and Marcel, Y. L., et al., “The epitopes of apolipoproteinA-I define distinct structural domains including a mobile middleregion,” J. Biol. Chem. 266(6):3644-3653, 1991.

In another aspect, the invention provides a method for diagnosing and/orassessing the risk of CAD in a subject, comprising determining changesin a biomarker profile comprising the relative abundance of at leastone, two, three, four, five, ten or more biomarkers present in the HDLfraction of a biological sample from a test subject as compared to thepredetermined abundance of the at least one, two, three, four, five, tenor more biomarkers from a reference population of apparently healthysubjects. The biomarkers are selected from biomarkers set forth in TABLE3, TABLE 4, and TABLE 5. The biomarker profile may optionally include aninternal reference standard that is expected to be equally abundant insubjects with CAD and apparently healthy subjects.

In another aspect, the present invention provides a method fordetermining the efficacy of a treatment regimen for treating and/orpreventing CAD by monitoring a measurable feature of at least twobiomarkers selected from the group consisting of apoA-I, apoA-II,apoB-100, Lp(a), apoC-I, and apoC-III, combinations or portions and/orderivatives thereof in a subject during treatment for CAD. The treatmentfor CAD varies depending on the symptoms and disease progression. Thegeneral treatments include lifestyle changes, medications, and mayinclude surgery. Lifestyle changes include, for example, weight loss, alow saturated fat, low cholesterol diet, reduction of sodium, regularexercise, and a prohibition on smoking. Medications useful to treat CADinclude, for example, cholesterol-lowering medications, antiplateletagents (e.g., aspirin, ticlopidine, clopidogrel), glycoprotein IIb-IIIainhibitors (such as abciximab, eptifibatide or tirofiban), orantithrombin drugs (blood-thinners such as heparin) to reduce the riskof blood clots. Beta-blockers may be used to decrease the heart rate andlower oxygen use by the heart. Nitrates, such as nitroglycerin, are usedto dilate the coronary arteries and improve blood supply to the heart.Calcium-channel blockers are used to relax the coronary arteries andsystemic arteries and thus reduce the workload for the heart.Medications suitable for reducing blood pressure are also useful totreat CAD, including ACE inhibitors, diuretics, and other medications.

The treatment for cardiovascular disease may include surgicalinterventions such as coronary angioplasty, coronary atherectomy,ablative laser-assisted angioplasty, catheter-based thrombolysis,mechanical thrombectomy, coronary stenting, coronary radiation implant,coronary brachytherapy (delivery of beta or gamma radiation into thecoronary arteries), and coronary artery bypass surgery.

In another aspect, the present invention provides assays and kitscomprising one or more detection reagents for determining susceptibilityor presence of cardiovascular disease in a mammalian subject based onthe detection of at least one measurable feature of at least onebiomarker in a biological sample, an HDL subfraction thereof, or acomplex containing apoA-I or apoA-II isolated from the biologicalsample. The biomarker is detected by mixing a detection reagent thatdetects at least one biomarker associated with CAD with a samplecontaining HDL-associated proteins (either an HDL subfraction or acomplex containing apoA-I or apoA-II) and monitoring the mixture fordetection of the biomarker with a suitable detection method such asspectrometry, immunoassay, or other method. In one embodiment, theassays are provided as a kit. In some embodiments, the kit comprisesdetection reagents for detecting at least two, three, four, five, ten ormore HDL-associated biomarkers in biological samples from a testsubject.

The kit also includes written indicia, such as instructions or otherprinted material for characterizing the risk of CAD based upon theoutcome of the assay. The written indicia may include referenceinformation or a link to information regarding the predeterminedabundance of the at least one, two, three, four, five, ten or moreHDL-associated biomarkers from a reference population of apparentlyhealthy subjects and an indication of a correlation between theabundance of one or more HDL-associated biomarkers and the risk leveland/or diagnosis of CAD.

The detection reagents may be any reagent for use in an assay oranalytical method, such as mass spectrometry, capable of detecting atleast one measurable feature of at least one biomarker selected from thegroup consisting of apoA-I, apoA-II, apoB-100, Lp(a), apoC-I, andapoC-III. In another embodiment, the detection reagents include proteinswith peptides identical to those of apoA-I, apoA-II, apoB-100, Lp(a),apoC-I, and apoC-III, such as peptides provided in TABLE 3. A variety ofprotocols for measuring the relative abundance of the biomarkers may beused, including mass spectrometry, ELISAs, RIAs, and FACs, which arewell known in the art.

In one embodiment, the detection reagent comprises one or moreantibodies which specifically bind one or more of the biomarkersprovided in TABLE 3, TABLE 4, or TABLE 5 that may be used for thediagnosis and/or prognosis of CAD characterized by the relativeabundance of the biomarker in the serum, or an HDL subfraction thereof.Standard values for protein levels of the biomarkers are established bycombining biological samples taken from healthy subjects. Deviation inthe amount of the biomarker between control subjects and CAD subjectsestablishes the parameters for diagnosing and/or assessing risk levels,or monitoring disease progression. The biomarkers and fragments thereofcan be used as antigens to generate antibodies specific for the CADbiomarkers for use in immunodiagnostic assays. Purified samples of thebiomarkers comprising the amino acid sequences shown in TABLE 3, TABLE4, and TABLE 5 may be recovered and used to generate antibodies usingtechniques known to one of skill in the art.

In another embodiment, the detection reagent comprises isotope-labeledpeptides, such as one or more of the peptides described in TABLE 3,TABLE 4, and TABLE 5 that correspond to the biomarker to be detected. Inaccordance with this embodiment, the kit includes an enzyme, such astrypsin, and the amount of the biomarker in the tryptic digest of thesample is then quantified by isotope dilution mass spectrometry. Thelabeled peptides may be provided in association with a substrate, andthe assay may be carried out in a multiplexed format. In one embodiment,a multiplexed format includes isotope-labeled peptides for at least twoor more of the HDL-associated biomarkers described herein that areenriched in HDL of subjects with established CAD. The peptides arequantified of all the HDL-associated peptides in a biological sampleobtained from a test subject using a technique such as isotope dilutionmass spectrometry. The detection and quantification of multipleHDL-associated biomarker proteins may be used to increase thesensitivity and specificity of the assay to provide an accurate riskassessment and/or diagnosis of the presence of CAD in the test subject.

In one embodiment of the kit, the detection reagent is provided inassociation with or attached to a substrate. For example, a sample ofblood, or HDL subfraction thereof, may be contacted with the substrate,having the detection reagent thereon, under conditions that allowbinding between the biomarker and the detection reagent. The biomarkerand/or the detection reagent are then detected with a suitable detectionmethod. The substrate may be any suitable rigid or semirigid supportincluding membranes, filters, chips, slides, wafers, fibers, magnetic ornonmagnetic beads, gels, tubing, plates, polymers, microparticles, andcapillaries. The substrate can have a variety of surface forms, such aswells, trenches, pins, channels, and pores to which the polypeptides arebound. For example, a chip, such as a biochip, may be a solid substratehaving a generally planar surface to which a detection reagent isattached. For example, a variety of chips are available for the captureand detection of biomarkers, in accordance with the present invention,from commercial sources such as Ciphergen Biosystems (Fremont, Calif.),Packard BioScience Company (Meriden, Conn.), Zyomyx (Hayward, Calif.),and Phylos (Lexington, Mass.). An example of a method for producing sucha biochip is described in U.S. Pat. No. 6,225,047. The biomarkers boundto the substrates may be detected in a gas phase ion spectrometer. Thedetector translates information regarding the detected ions intomass-to-charge ratios. Detection of a biomarker also provides signalintensity, thereby allowing the determination of quantity and mass ofthe biomarker.

The following examples merely illustrate the best mode now contemplatedfor practicing the invention, but should not be construed to limit theinvention.

Example 1

This example demonstrates that subjects may be successfully classifiedas normal control or coronary artery disease (CAD) subjects by analyzingthe proteomic profile of HDL₂ tryptic peptides using matrix-assistedlaser desorption ionization (MALDI) time-of-flight (TOF) tandem massspectrometry (MALDI-TOF-MS) and subjecting the results to principalcomponent analysis (PCA), a well-established pattern recognition method.

Rationale:

The overall approach in this study was to isolate HDL2 from control andCAD subjects, analyze a tryptic digest of HDL proteins by MALDI-TOF-MS,and use pattern recognition of the full scan mass spectra to classifysubjects as either CAD subjects or control subjects.

Methods:

Sample Isolation and Preparation:

All protocols involving human subjects were approved by the HumanStudies Committees at the University of Washington Blood samples werecollected from 20 apparently healthy adult males and from 18 malepatients with established CAD after an overnight fast. Blood sampleswere anticoagulated with EDTA. All subjects were male and matched forage and HDL cholesterol (HDL-C) levels. The CAD subjects had documentedvascular disease, with symptoms consistent with angina and abnormal Qwaves on their EKG or at least one stenotic lesion (>50% occlusion oncoronary angiography). These CAD subjects were clinically stable, atleast three months had elapsed since their acute coronary syndrome, andthey had not taken lipid-lowering drugs for the six weeks prior to bloodcollection. The control subjects were apparently healthy and had noknown history of CAD, were not hyperlipidemic, had no family history ofpremature CAD, and were not receiving any lipid-lowering therapy. Noneof the control subjects smoked cigarettes, had liver or renal disease,were diabetic, or had received lipid-lowering medications for at leastsix weeks before blood was collected.

The clinical characteristics of the two subject populations aresummarized below in Table 1.

TABLE 1 CLINICAL CHARACTERISTICS OF STUDY SUBJECTS CholesterolTriglycerides HDL-C LDL-C Number Status Age (yr) % Male (mg/dl) (mg/dl)(mg/dl) (mg/dl) 20 Control 57 (6) 100 197 (13) 104 (29) 42 (8) 134 (14)18 CAD 57 (6) 100 223 (27) 146 (67) 41 (8) 160 (25)

It is noted that although levels of plasma LDL and triglycerides werehigher in the CAD subjects than in the control subjects, the two groupswere otherwise well matched for known risk factors for vascular disease.

HDL Isolation:

HDL₂ (d=1.063 to 1.125 g/mL) was isolated from plasma obtained from theblood samples by sequential density ultracentrifugation, according tothe methods described in Mendez. A. J., et al., J. Biol. Chem.266:10104-10111, 1991. Protein concentration of HDL was determined usingthe Bradford assay (BioRad, Hercules, Calif.) with albumin as thestandard.

Tryptic Digestion:

HDL₂ was digested for 60 minutes with trypsin (1:50 w/w trypsin/HDL,sequencing grade trypsin, Promega Wis.) in 100 mM ammonium bicarbonatebuffer in 80% aqueous acetonitrile (Strader, M. B., et al., Anal. Chem.78(1):125-134, 2006). Digestion was terminated by addition oftrifluoroacetic acid (TFA) to 1% final concentration.

The protein concentration of the HDL₂ digest was adjusted to 100 ng/μLwith matrix solvent (70% acetonitrile, 0.1% TFA), and 0.5 μl of thedigest was deposited on a MALDI target plate. Dried spots were overlaidwith 0.5 μL of MALDI matrix (5 mg/mL alpha-cyano-4-hydroxy-cinnamic acid(CHCA) in matrix solvent.

Mass Spectrometric Analysis.

Mass spectra were acquired on a matrix-assisted laser desorptionionization (MALDI) time-of-flight (TOF) tandem mass spectrometer(Applied Biosystems 4700 Proteomics Analyzer), operated in thereflection mode. Raw spectra were (i) baseline-corrected and centroidedusing algorithms supplied by the manufacturer (ABI 4700 Explorersoftware, version 3.5); and (ii) internally mass calibrated using 5tryptic fragments of apolipoprotein AI (apoA-I). The centroided spectrawere then exported, using T2Extractor 5(http://www.proteomecommons.org/archive/1114637208624/) for furtheranalysis. It was determined that internal calibration afforded massaccuracy better than 5 ppm across the acquisition mass range.

For pattern recognition analysis, a single mass spectrum was generatedfrom at least 80 sub-spectra generated randomly from different sitesacross the sample spot, each sampled with 25 laser shots, for a total of2,000 shots. To exclude low intensity and saturated sub-spectra, onlythose with an ion current ranging from 30 to 80×10⁴ cps were used toproduce the mass spectrum.

The analytical precision of the different steps was evaluated byacquiring multiple spectra from (1) the same MALDI spot; (2) multipleMALDI spots of the same tryptic digest; (3) multiple tryptic digests ofthe same sample; and (4) tryptic digestion of the same sample carriedout on different days. As shown in FIGS. 9A-D, precision analysis ofindividual mass channels showed excellent reproducibility of the spectrafrom the same spot (FIG. 9A), multiple spots (FIG. 9B), paralleldigestions (FIG. 9C), and interday digestion (FIG. 9D). The dataindicated that precision was improved by averaging several spectra fromthe same spot. Thus, for the PLS-DA analysis, four spectra from the samespot were averaged to generate a master spectrum used for subsequentanalyses.

Processing of MS Spectra:

MATLAB (version 7.0, MathWorks Inc.) was used for pattern recognitionanalysis. The full scan mass spectrum of each sample was transformedinto a vector format suitable for pattern recognition based on linearalgebra by placing the signals in bins that ranged from m/z 800 to m/z5,000. To ensure that every spectrum had the same mass channels, binsizes were increased linearly over this range to yield 45,920 bins orchannels per spectrum. After binning, data vectors were aligned toremove single bin shifts that occurred when signals were near the binboarder. A threshold of 1/10,000 of the spectrum's total signal was usedto remove baseline noise, and the spectra were aligned. For PLS-DA thedata were separated into calibration and test sets prior topreprocessing to avoid overfitting. After alignment and filtering, 2,338channels contained signals.

PCA Analysis:

Processed MS spectra were then subjected to principal component analysis(PCA) and PLS-DA (Beebe, K. R., et al., Chemometrics: A Practical Guide.New York: Wiley-Interscience, 1998; and International Publication No.WO2006/083852, incorporated by reference herein in its entirety). PCAwas used to assess the reproducibility of MALDI plate spotting anddigestion and the sensitivity of MALDI-TOF-MS to changes in HDL proteincomposition. The latter was tested by mixing variable amounts of HDL₂isolated from CAD and control subjects, as shown in FIG. 8.

Validation of the Pattern Recognition Model:

In order to test the ability of pattern recognition to distinguishbetween CAD and control HDL, PCA analysis was performed after mixingvariable proportions of HDL₂ isolated from control and CAD subjects.Mass spectra of 6 pairs of randomly chosen CAD and control samples weremixed in ratios of 1:0, 1:3, 1:1, 3:1, and 0:1. In separate experiments,blinded test samples from CAD or control subjects mixed at the sameratios were also included for the study.

Results:

PCA is a powerful linear algebraic technique for identifying factorsthat differentiate populations in a complex data set (Martens, H., etal., Multivariate Calibration. New York: John Wiley & Sons, 1989;Marengo, E., et al., Proteomics 5(3):654-666, 2005; Lee, K. R., et al.,Proteomics 3(9):1680-1686, 2003; Natale, C. D., et al., BiosensorsBioelectron. 18(10):1209-1218, 2003). Importantly, this unsuperviseddata reduction method creates pattern recognition models without apriori assumptions regarding relationships between individual samples(Beebe, K. R., et al., Chemometrics: A Practical Guide. New York:Wiley-Interscience, 1998).

PCA was initially used to test the ability of pattern recognition todistinguish between CAD and control HDL after mixing variableproportions of HDL₂ isolated from control and CAD subjects. The resultsof this analysis are shown in FIG. 8, where the square symbols andtriangle symbols represent different pairs of CAD and control samplesmixed at different ratios. As shown in FIG. 8, the bottom left corner ofthe graph shows control samples and the upper right corner of the graphshows CAD samples. Circles around the symbols represent a group ofspectra from different mixed ratios. As shown in FIG. 8, the control andCAD subjects were well separated by PCA analysis. When the relativeproportion of HDL₂ protein derived from control and CAD subjects in eachsample was varied, there was a clear shift in the location of eachsample on the PCA plot, as shown in FIG. 8, indicating that the methodis sensitive to differences in the protein composition of HDL.Furthermore, the tight clustering of replicate spectra of the sampledemonstrates the precision of this method.

These results demonstrate that subjects can be classified as CADsubjects based on the protein composition of their HDL₂ which differssubstantially as compared to the protein composition of HDL₂ isolatedfrom control subjects. These results further demonstrate that HDL₂ fromsubjects may be successfully classified into CAD or control subjectsbased on the MALDI-TOF-MS and PCA-based pattern profiling described. Useof tryptic peptides significantly enhances the precision and probabilityof identifying proteins and post-translational modifications and allowsrapid analysis. Furthermore, as shown in FIG. 9, the tight clustering ofreplicate spectra demonstrates the precision of the analytical method.Thus, PCA provides a fast, simple, exploratory, and qualitative measureof differences in the protein cargo of HDL₂.

Example 2

This example demonstrates that subjects may be successfully classifiedinto CAD or normal control subjects by analyzing tryptic digests ofHDL₂-associated proteins by MALDI-TOF-MS using a highly precise patternrecognition linear algebraic algorithm, partial least squaresdetermination analysis (PLS-DA).

Rationale:

Although PCA is a powerful technique for detecting and visualizingdifferences in patterns, it does not provide quantitative measures forpredicting the disease status of individual samples. Therefore, anotherpowerful linear algebraic technique, partial least squares discriminateanalysis (PLS-DA) was used to develop a quantitative approach toclassifying subjects with regard to CAD disease status. PLS-DA wasselected rather than other pattern recognition techniques (such asK-nearest neighbor and Support Vector Machine) because it is well suitedto analyzing the quantitative information in a mass spectrum whichcontains multiple independent signals as well as signals withsignificant redundancy and signals with incomplete selectivity.

Methods:

Sample Isolation and Preparation.

HDL₂ fractions were isolated from the blood plasma of CAD and controlsubjects and digested with trypsin for 60 minutes, as described inExample 1. A sample from each subject was individually analyzed usingMALDI-TOF MS as described in Example 1.

Processing of MS Spectra Using Partial Least Squares DiscriminateAnalysis (PLS-DA) Analysis.

Matlab (version 7.0, Mathworks Inc.) was used for pattern recognitionanalysis. The full scan mass spectrum of each sample was transformed toa vector format suitable for pattern recognition based on linear algebraby placing the signals in bins that ranged from m/z 800 to m/z 5,000. Toensure that every spectrum had the same mass channels, bin sizes wereincreased linearly over this range to yield 45,920 bins or channels perspectrum. After binning, data vectors were aligned to remove single binshifts that occurred when signals were near the bin boarder. A thresholdof 1/10,000 of the spectrum's total signal was used to remove baselinenoise, and the spectra were aligned. For PLS-DA, the data were separatedinto calibration and test sets prior to preprocessing to avoidoverfitting. After alignment and filtering, 2,338 channels containedsignals.

Preprocessed MS spectra were then subjected to PLS-DA. PLS-DA is asupervised pattern recognition technique. It uses two sets of data, suchas training sets with defined groups (such as cases vs. controls) to“supervise” the creation of a pattern recognition model (Barker, M., etal., J. Chemometr. 16:166-173, 2003), which is subsequently applied to asecond test set of samples of unknown status. Thus, PLS-DA can be usedto determine if a new proteomics sample belongs to previously definedsample classes. Furthermore, it can reveal relationships among sampleclasses and identify features distinguishing the classes, and ultimatelythe corresponding proteins. Most importantly, PLS-DA yields a singlediscriminant score that quantifies similarity of the tested spectrumwith the model and can be used to predict the disease status ofindividual samples (CAD or control).

PLS-DA models were built with a dummy response matrix containingdiscrete numerical values (1 or −1) for each class as described inInternational Publication No. WO2006/083853. In the present analysis,“1” represented the CAD class and represented the control class. Foreach sample being classified, the PLS-DA model then produced adiscriminant value, which was termed the “proteomics CAD risk score” or“ProtCAD” score. The ProtCAD risk scores thus generated were used topredict disease status of the remaining control and CAD subjects(validation group) as described in International Publication No.WO2006/083853, hereby incorporated by reference.

To provide a quantitative estimate of the performance of the PLS-DAmodel, two approaches were used to provide a quantitative estimate ofthe performance: (1) Random permutation analysis; and (2) Leave-one-outanalysis.

Random Permutation Analysis:

When data from only a small number of subjects are used to build acomplex pattern recognition model, predictions are often affected by theselection of the calibration subjects. Therefore, a permutation analysiswas used to test the ProtCAD score's ability to predict disease status.In each step of this analysis, the subjects were assigned to two groups:a calibration group and a validation group, each composed of tenrandomly selected control subjects and nine CAD subjects. Thecalibration group was used to build a PLS-DA model, which was then usedto predict the ProtCAD score for each subject in the validation group.This process was repeated 7,777 times to determine the precision of thePLS-DA, predictions, as described in international Publication No.WO2006/083853.

Receiver Operating Characteristics (ROC) Curves:

Nonparametric empirical receiver operating characteristic (ROC) curveswere constructed from the ProtCAD risk score (Pepe, M. S., TheStatistical Evaluation of Medical Tests for Classification andPrediction, New York, Oxford University Press, 2003). Sensitivity andspecificity were calculated from the known class identity of eachsubject in the validation group. Area under the curve (AUC) calculationswas determined using the trapezoidal rule (Fawcett, T., “An IntroductoryROC Analysis,” Pattern Recognition Letters 27:861-874, 2006). For eachpermutation, one ROC curve was generated, and by plotting thesensitivity (fraction of positive results) against specificity (thefraction of negative results), ROC quantitatively assessed the accuracyof the predictive test. A quantitative PLS-DA model based on full scanmass spectra of HDL₂ from a calibration group randomly selected subjectspredicted. CAD status in the validation group, with an average ROC_(AUC)of 0.9. ROC_(AUC) of 0.5 represents chance discrimination, whereasROC_(AUC) 1.0 represents perfect discrimination. For a CAD diagnostictest, an ROC_(AUC) of 0.7-0.8 is generally considered acceptable, andvalues over 0.8 are considered excellent.

Leave-One-Out ProtCAD Prediction:

In order to use the maximum number of available subjects, aleave-one-out approach was utilized to build a more powerful PLS-DAmodel, as described in International Publication No. WO2006/083853. TheProtCAD score for each subject was determined using a model built fromthe remaining subjects (e.g., 17 CAD and 19 controls). To predict thedisease status of a subject, a ROC curve was first constructed and thenthe value of the ProtCAD score was compared to a threshold valuecorresponding to a selected sensitivity and selectivity on the ROCcurve.

In preliminary experiments, it was determined that CAD predictions basedon the entire mass spectrum outperformed predictions based on the tenmost selective signals (as determined by PLS-DA, data not shown). Fullspectrum PLS-DA can identify signals in regions normally associated withlow selectivity and help identify outlier samples (e.g., problems withdata acquisition from MS analysis, sample handling) or marked variationsin sample protein composition (e.g., genetic variations in apoA-I,post-translational modifications). Such outliers would be overlooked bytechniques that use only selected features of the spectrum. Therefore,all of the information in the full scan mass spectra was used to buildmodels.

Results:

Two approaches were used to assess the ability of PLS-DA to distinguishthe proteomic fingerprints of HDL isolated from control and CADsubjects. First, a PLS-DA model was built using data from randomlyselected control and CAD subjects. Then the model was tested for itsability to predict disease status in a second set of subjects. ThePLS-DA models were built with a dummy response matrix containingdiscrete numerical values for each class using ten control and nine CADsubjects (the calibration set) that were randomly selected from the 20control and 18 CAD subjects. The PLS-DA model was then used to predictthe disease status of the remaining ten control and nine CAD subjects(the validation set). For each sample in the validation set the approachproduced a discriminant score, which was termed the “Proteomics CAD riskscore” (ProtCAD risk score).

Random permutation analysis was used to provide a quantitative estimateof the performance of the PLS-DA model that was used to build thediscriminant termed the Proteomics CAD risk score (ProtCAD risk score).The ProtCAD score was then used to predict the CAD status of thevalidation group (the remaining nine CAD and ten control subjects). Atotal of 7,777 random permutations were used to construct the ROC curve.FIG. 1 presents graphical results demonstrating the receiver operatingcharacteristic (ROC) curve of the prediction of cardiovascular disease(CAD) status based on random permutation analysis. By plottingsensitivity (the fraction of true positive results, shown in the y-axis)against specificity (the fraction of true negative results, shown in thex-axis), the ROC curve quantitatively assesses the accuracy of apredictive test. As shown in FIG. 1, the average ROC curve shows anarea-under-the-curve of 0.880±0.097 (mean, SD. N=7,777 iterations),indicating that PLS-DA analysis can predict disease status with highsensitivity and specificity.

FIG. 2 graphically illustrates the prediction of CAD status by theproteomics CAD risk score “ProtCAD risk score” using a partial leastsquares discriminate analysis (PLS-DA) model built using a calibrationgroup. Using a sensitivity of 80%, the ProtCAD risk score of eachsubject in the validation group at each permutation was used to predicttheir CAD status. The results in FIG. 2 demonstrate that the ProtCADscore generated using the PLS-DA model is able to distinguish CAD andcontrol subjects with high selectivity (p-value of 0.0001%). Using aclinically acceptable sensitivity of 80% (see Pepe, M. S., TheStatistical Evaluation of Medical Tests for Classification andPrediction, New York: Oxford University Press, 2003), the average PLS-DAmodel predicted CAD status with 76% specificity, as shown in FIG. 2.

The level of specificity shown in FIG. 2 corresponds to an odds ratio of12.7, i.e., the odds ratio of the PLS-DA model for predicting CAD statushere was 12.7 at 80% sensitivity and 76% specificity. These resultsdemonstrate the power of this analytical approach for identifyingsubjects at risk for CAD.

In the second approach, disease status was predicted by using PLS-DAmodels built with the leave-one-out method as described in InternationalPublication No. WO2006/083853. This strategy allowed the use of allavailable subjects for the analysis, which would be expected to yieldthe strongest predictive model. After systematically leaving out onesubject at a time from the calibration set, the subject's ProtCAD scorewas predicted using a model built from the remaining samples.

FIG. 3 graphically illustrates the power of the ProtCAD risk score todiscriminate between the CAD samples and healthy control samples basedon leave-one-out analysis. The ProtCAD risk score was derived fromPLS-DA analysis of MALDI-TOF-MS mass spectra of HDL tryptic digests,using a leave-one-out experiment for all 18 CAD and 20 control subjects.These ProtCAD scores distinguished the CAD and control subjects withhigh selectivity (p<0.0001), as shown in FIG. 3. Furthermore, the largernumber of subjects in the calibration set improved diagnostic power. Asshown in FIG. 10A, the ROC curve constructed for ProtCAD scores from theleave-one-out approach showed an area-under-the-curve of 0.94 and amaximum odds ratio of 68. From the leave-one-out ProtCAD risk score ROCcurve, we determined a threshold corresponding to 90% sensitivity(ProtCAD threshold=−0.06). Using this threshold, the model correctlyclassified 16 of 18 CAD subjects and 19 of 20 control subjects.

Therefore, these results demonstrate that pattern recognition analysis,when applied to MALDI-TOF-MS spectra of tryptic digests of HDL₂, clearlydemonstrate differences in the HDL proteomic signature of apparentlyhealthy subjects and CAD subjects. Moreover, a quantitative PLS-DA modelbased on full scan mass spectra of HDL₂ from a calibration group ofrandomly selected subjects predicted CAD status in the validation group,with an average ROC_(AUC) of 0.9 (ROC_(AUC) 0.5 represents chancediscrimination, whereas ROC_(AUC) 1.0 represents perfectdiscrimination).

For a CAD diagnostic test, an ROCAUC of 0.7 to 0.8 is generallyconsidered acceptable, and values over 0.8 are considered excellent (seePepe, M. S., The Statistical Evaluation of Medical Tests forClassification and Prediction, New York, Oxford University Press, 2003).Furthermore, the odds ratio of the PLS-DA model for predicting CADstatus was 12.7 at 80% sensitivity and 76% specificity. When the modelwas built with data from a larger number of subjects using theleave-one-out method, the ProtCAD risk score distinguished subjects withan even higher odds ratio of 68. These results compare favorably withother single lipoprotein-associated risk factors identified in previousstudies (Yusuf, S., et al., Lancet 364(9438):937-952, 2004; Walldius,G., and I. Jungner, J. Intern. Med. 259(5):493-519, 2006; Walldius, G.,and I. Jungner, Curr. Opin. Cardiol. 22(4)359-367, 2007).

The standard method for predicting CAD, the Framingham risk score,combines seven demographic, biochemical and medical factors to predictCAD risk (Wilson, P. W., et al., Circulation 97(18):1837-1847 (1998)).The Framingham risk scores ROC_(AUC) ranges from 0.6-0.8 for predictingCAD risk over a ten year period. Its strongest predictors are age andsex, which are not modifiable risk factors. Moreover, LDL-C and HDL-Ccontribute little to the risk score in some models (Yusuf, S., et al.,Lancet 364(9438):937-952, 2004; Walldius, G., et al., Curr. Opin.Cardiol. 22(4):359-367, 2007; Walldius, G., et al., J. Intern. Med.259(5):493-519, 2006).

On the other hand, this example indicates that the HDL₂ isolated fromCAD subjects with its characteristic proteome profile is faster and moreaccurate at predicting risk with a ROC_(AUC) of 0.9.

Conclusion:

As demonstrated herein, the protein composition of the HDL₂ in subjectswith CAD as well as the protein composition of HDL₂ isolated fromcontrol subjects are different. Differences in the protein profiles canbe accurately and quantitatively measured using the two differentapproaches together with the PLS-DA algorithm. These observations alsoshow that PLS-DA analysis can correctly and with high sensitivitypredict the status of a subject as a CAD subject or a control subject.

The methods of proteomic fingerprinting of HDL by MALDI-TOF-MS offer anumber of important advantages for building classification models.First, it has been demonstrated that HDL is causally linked to CADpathogenesis. Second, the HDL proteome is much simpler than the plasmaproteome (which has been estimated to contain >10⁴ different proteinsand peptides with relative concentrations ranging over 12 orders ofmagnitude), which greatly facilitates MS analysis. Third, theinterrogation of tryptic digests significantly enhances the precision ofthe mass spectrometric measurements, and thereby increases theprobability of identifying proteins and post-translationalmodifications. In contrast to the methods described herein,surface-enhanced laser desorption ionization (SELDI) MS analysis, whichhas been widely used for pattern recognition, typically samples intactproteins, which makes it difficult to identify the proteins responsiblefor informative signals in quantitative models, SELDI MS also has alimited mass range and low mass resolution, which bias detection ofinformative features toward degraded and low MW proteins. Finally, thehigh mass accuracy of MALDI-TOE-MS facilitates the subsequentidentification of proteins and posttranslational modifications by tandemMS. MALDI-TOF-MS of tryptic digests also greatly improves the precisionof signals, which is important for pattern recognition analysis.

Previous studies using shotgun proteomics to investigate the proteincomposition of HDL₃ using liquid chromatography in concert withelectrospray ionization (ESI) to introduce peptides into the massspectrometer (Vaisar, T., et al., J. Clin. Invest. 117(3):746-756(2007)). In contrast, in the present study the peptides were ionizedwith MALDI. It is well established that ESI and MALDI ionize differentclasses of peptides with different efficiencies. For example,hydrophobic peptides are much more readily introduced into the gas phaseby MALDI.

Example 3

This example describes the identification of proteins differentiallypresent in HDL₂ subfractions isolated from normal control and CADsubjects by tryptic peptide analyses of HDL₂ fractions by tandem massspectrometric (tandem MS) following PLS-DA based pattern recognitionprofiling.

Methods:

Sample Isolation and Preparation:

HDL₂ fractions were isolated from normal control and CAD subjects asdescribed earlier in Example 1. Tryptic digests of HDL₂ fractions weresubjected to liquid chromatographic separation with direct applicationof the sample effluent from the liquid chromatograph onto a MALDI sampleplate and subjected to MALDI-TOF/TOF tandem mass spectrometric analysis(LC-MALDI-TOF/TOF). Subjects were confirmed as either CAD or normalsubjects by pattern recognition proteomic profiling of HDL₂ proteinsusing PLS-DA, as described in Example 2.

The PLS-DA models are characterized by regression vectors which indicatechannels on the m/z axis of a mass spectrum that differentiate the twosample classes.

FIG. 4 graphically illustrates the PLS-DA regression vectors (y-axis) ofthe leave-one-out PLS-DA model that distinguish CAD and controlsubjects. The x-axis (m/z) represents mass channels of the MALDI-TOFmass spectrum. Positive and negative features on the regression vectorindicate an increase and decrease of the signals from CAD samples (andtherefore relative amount of peptide present) relative to controlsamples. Each mass channel in the regression vector that had significantdifferences between CAD and normal subjects was called an informativefeature.

Channels in the regression vectors with positive values correspond tothe peptides (and indirectly to the proteins) with increased relativeabundance in CAD samples. Channels with negative values in theregression vector have decreased abundance in CAD samples. As shown inFIG. 4, a subset of 13 informative features were identified with themost significant increase or decrease in CAD subjects as compared tonormal control subjects in a full scan mass spectrum that contributed tothe ability to differentiate CAD subjects from normal subjects. Thepeptides associated with these informative features were identified bytandem MS using the MALDI-TOF/TOF analyzer capable of MS and MS/MSanalysis interfaced with an off-line capillary liquid chromatograph andcoupled with a MALDI plate spotter. As described in InternationalPublication No. WO2006/083853, chromatographic information may be usedto more strongly validate that the peptide identified is actuallyproducing the observed peak in the regression vector.

Identification of Significant Features by Liquid-Chromatography (LC)Matrix-Assisted Laser Desorption Ionization (MALDI):

To identify features that were enriched or depleted in the mass spectraof HDL isolated from CAD subjects, CAD HDL tryptic digests werefractionated by liquid chromatography and the peptide digest wassubjected to MS/MS analysis by MALDI-TOF/TOF. A tryptic digest of HDLwas injected onto a trap column (NanoTrap C18, LC Packings), washed, andeluted onto an analytical capillary HPLC column (PepMap C18, LCPackings) using an Ultimate 300 (LC Packings Inc.). Separation wasachieved by linear gradient 5-50% B over 40 minutes (A-5% aqueous can,0.1% TFA, B-80% aqueous can, 0.1% TFA). The eluent from the column wasmixed with matrix (CHCA, 5 mg/ml in 70% ACN) containing internalstandard peptides and spotted on-line (Shimadzu Accuspot MALDI platespotter) on a MALDI target plate Targeted MS/MS analysis of selectedpeptide ions was based on informative mass features of HDL proteomicsfingerprints that were identified in the PLS-DA analysis. Peptides wereidentified by MASCOT database search (v2.0, Matrix Science) against thehuman SwissProt protein database with the following parameters: trypsincleavage with up to two missed cleavages, methionine oxidation variablemodification, precursor tolerance 15 ppm, and fragment ion tolerance 0.2Da. Peptide matches were only accepted if the MASCOT probability basedMowse score identified the peptide with a very high score indicating amatch to the database with >99% confidence.

Results:

It was determined that the relative abundance of HDL₂ apolipoproteinswas altered in CAD subjects compared to the controls.

One group of informative features arose from proteins in the HDL₂fraction that were differentially abundant in CAD and control subjects.As shown in FIG. 4, informative features with positive regression vectorvalues were observed at channels 1081, 1226, 1440, 1715*, 1904, 2203,and 2661 m/z in the CAD subjects relative to the control subjects,indicating that the peptides (and therefore proteins) represented bythese regression vectors at these channels were more abundant in CADsubjects.

As further shown in FIG. 4, informative features with negativeregression vector values were observed at channels 852*, 1012, 1302,1380, 1612, and 2645 m/z in the CAD subjects relative to the controlsubjects, indicating that the peptides (and therefore proteins)represented by these regression vectors at these channels were reducedin CAD subjects as compared to normal control subjects.

TABLE 2 provides a set of informative biomarkers corresponding tofeatures from FIG. 4 that were identified using the targetedLC-MALDI-TOF/TOF approach.

TABLE 2 BIOMARKERS IDENTIFIED AS PROGNOSTIC AND/OR DIAGNOSTIC INDICATORSOF CAD Protein Refseq ID Numbers SEQ ID NO: apoA-I NP_000030.1 SEQ IDNO: 1 apoA-II NP_001634 SEQ ID NO: 2 apoB-100 NP_000375 SEQ ID NO: 3Lp(a) NP_005568.1 SEQ ID NO: 4 apoC-I NP_001636.1 SEQ ID NO: 5 apoC-IIINP_000031.1 SEQ ID NO: 6 SAA4 (serum amyloid A4- NP_006503 SEQ ID NO: 7confirm) apoE NP_000032 SEQ ID NO: 8

Targeted tandem MS analysis was carried out to identify the peptidescorresponding to the informative features shown in FIG. 4. The resultsare shown in TABLE 3.

TABLE 3 PEPTIDES IDENTIFIED AS INFORMATIVE FOR CAD Magnitude in SEQRegression ID m/z Vector Peptide Protein start-stop NO 861.5088 −11.679ITLPDFR apoB-100 2706-2712 9 999.5271 −5.9951 SVGFHLPSR apoB-1001325-1333 10 1012.6055 −39.9366 AKPALEDLR apoA-I 231-239 11 1013.5781−21.0822 AKPALEDLR-I apoA-I 231-239 11 1014.5921 −5.15 AKPALEDLR-IIapoA-I 231-239 11 1031.5333 −5.700 LSPLGEEMR apoA-I 165-172 12 1032.524−3.785 LSPLGEEMR-I apoA-I 165-173 12 1033.5571 5.470 LSPLGEEMR-II apoA-I165-173 14 1033.5571 5.470 LQAEAFQAR apoE 270-278 13 1047.4997 −6.987LSPLGEEMoxR apoA-I 165-173 12 1048.5057 −3.295 LSPLGEEMoxR-I apoA-I165-173 12 1049.5128 −1.862 LSPLGEEMoxR-II apoA-I 165-173 12 1081.604322.482 LAAYLMLMR apoB-100 559-567 15 1141.6155 2.912 HINIDQFVR apoB-1002101-2109 16 1156.6456 −3.813 SKEQLTPLIK apoA-II 68-77 17 1156.6456−3.813 SPAFTDLHLR apoB-100 3980-3989 18 1157.6638 −22.261 LEALKENGGARapoA-I 202-212 19 1157.6638 −22.261 SKEQLTPLIK-I apoA-II 68-77 171158.6367 −15.025 LEALKENGGAR-I apoA-I 202-212 19 1158.6367 −15.025SKEQLTPLIK-II apoA-II 68-77 17 1159.6567 −5.85 LEALKENGGAR-II apoA-I202-212 19 1159.6567 −5.85 SKEQLTPLIK-III apoA-II 68-77 17 1160.6312−0.846 LEALKENGGAR-III apoA-I 202-212 19 1160.6312 −0.846 SKEQLTPLIK-IVapoA-II 68-77 17 1166.5888 −8.57 FRETLEDTR apoB-100 2512-2520 201167.5691 −5.47 FRETLEDTR-I apoB-100 2512-2520 20 1168.597 −1.685FRETLEDTR-II apoB-100 2512-2520 20 1169.579 −10.437 SLDEHYHIR apoB-1002211-2219 21 1170.6086 −7.161 SLDEHYHIR apoB-100 2211-2219 21 1171.5924−1.733 SLDEHYHIR apoB-100 2211-2219 21 1178.6429 −6.301 VLVDHFGYTKapoB-100 733-742 22 1179.6334 −5.424 VLVDHFGYTK-I apoB-100 733-742 221199.6662 −6.224 VKSPELQAEAK apoA-II 52-62 23 1200.6743 −4.688VKSPELQAEAK-I apoA-II 52-62 23 1201.6352 −4.73 VKSPELQAEAK-II apoA-II52-62 23 1201.6352 −4.73 LTISEQNIQR apoB-100 335-344 24 1202.645 −2.364VKSPELQAEAK-III apoA-II 52-62 23 1202.645 −2.364 LTISEQNIQR-I apoB-100335-344 24 1226.547 31.827 DEPPQSPWDR apoA-I 25-34 25 1227.5777 22.165DEPPQSPWDR-I apoA-I 25-34 25 1283.6171 17.33 WQEEMELYR apoA-I 132-140 261284.6444 13.139 WQEEMELYR-I apoA-I 132-140 26 1285.6211 4.945WQEEMELYR-II apoA-I 132-140 26 1286.5985 −1.058 WQEEMELYR-III apoA-I132-140 26 1299.5808 4.112 WQEEMoxELYR apoA-I 132-140 26 1300.5688 4.532WQEEMoxELYR-I apoA-I 132-140 26 1301.6617 −16.664 THLAPYSDELR apoA-I185-195 27 1302.6514 −82.138 THLAPYSDELR-I apoA-I 185-195 27 1302.6514−82.138 LSPLGEEMRDR apoA-I 165-175 28 1303.6417 −50.00 THLAPYSDELR-IIapoA-I 185-195 27 1303.6417 −50.00 LSPLGEEMRDR-I apoA-I 165-175 281304.685 −25.542 KGNVATEISTER apoB-100 196-207 29 1305.6769 −7.513KGNVATEISTER-I apoB-100 196-207 29 1306.6696 −0.949 KGNVATEISTER-IIapoB-100 196-207 29 1318.6407 2.632 LSPLGEEMoxRDR apoA-I 165-175 281319.6432 1.355 LSPLGEEMoxRDR-I apoA-I 165-175 28 1380.7137 −20.692VQPYLDDFQKK apoA-I 121-131 30 1381.7081 −15.84 VQPYLDDFQKK apoA-I121-131 30 1400.6834 6.728 DYVSQFEGSALGK apoA-I 52-64 31 1401.69224.3536 DYVSQFEGSALGK-I apoA-I 52-64 31 1402.7018 −2.083 DYVSQFEGSALGK-IIapoA-I 52-64 31 1403.7121 −4.059 DYVSQFEGSALGK-III apoA-I 52-64 311404.7231 −2.216 DYVSQFEGSALGK-III apoA-I 52-64 31 1410.748 −6.002FQFPGKPGIYTR apoB-100 4202-4213 32 1411.7077 −12.606 KWQEEMELYR apoA-I131-140 33 1412.7244 −1.039 KWQEEMELYR-I apoA-I 131-140 33 1412.7244−1.039 DPDRFRPDGLPK SAA4 117-128 34 1413.6854 −1.114 KWQEEMELYR-IIapoA-I 131-14- 33 1413.6854 −1.114 DPDRFRPDGLPK-I SAA4 117-128 341414.7036 0.290 KWQEEMELYR-III apoA-I 131-140 33 1414.7036 0.290DPDRFRPDGLPK-II SAA4 117-128 34 1415.7225 0.7011 KWQEEMELYR-IV apoA-I131-140 33 1415.7225 0.7011 DPDRFRPDGLPK-III SAA4 117-128 34 1427.664411.458 KWQEEMoxELYR apoA-I 131-140 33 1428.6927 9.069 KWQEEMoxELYR-IapoA-I 131-140 33 1429.6645 5.938 KWQEEMoxELYR-II apoA-I 131-140 331440.6864 48.538 NPDAVAAPYCYTR Lp(a) 79-91 35 1441.6664 35.366NPDAVAAPYCYTR-I Lp(a) 79-91 35 1442.7047 16.693 NPDAVAAPYCYTR-II Lp(a)79-91 35 1488.7235 −10.607 MREWFSETFQK apoC-I 64-74 36 1489.7361 −8.862MREWFSETFQK-I apoC-I 64-74 36 1490.6898 −3.125 MREWFSETFQK-II apoC-I64-74 36 1504.7079 −12.493 MoxREWFSETFQK apoC-I 64-74 36 1505.7314−12.066 MoxREWFSETFQK-I apoC-I 64-74 36 1506.6954 −5.215MoxREWFSETFQK-II apoC-I 64-74 36 1568.8737 9.352 LAARLEALKENGGAR apoA-I198-212 37 1569.8781 6.904 LAARLEALKENGGAR-I apoA-I 198-212 37 1585.84564.483 THLAPYSDELRQR apoA-I 185-197 38 1586.8608 0.9348 THLAPYSDELRQR-IapoA-I 185-197 38 1612.7768 −17.535 LLDNWDSVTSTFSK apoA-I 70-83 391716.8928 6.669 DALSSVQESQVAQQAR apoC-III 45-60 40 1717.8545 3.025DALSSVQESQVAQQAR-I apoC-III 45-60 40 1718.8855 −0.99 DALSSVQESQVAQQAR-IIapoC-III 45-60 40 1723.9809 2.583 QKVEPLRAELQEGAR apoA-I 141-155 411723.9809 2.583 IVQILPWEQNEQVK apoB-100 577-590 42 1724.9466 1.7167QKVEPLRAELQEGAR-I apoA-I 141-155 41 1724.9466 1.7167 IVQILPWEQNEQVK-IapoB-100 577-590 42 1725.9818 1.707 QKVEPLRAELQEGAR-II apoA-I 141-155 411725.9818 1.707 IVQILPWEQNEQVK-II apoB-100 577-590 42 1775.9145 15.536NLQNNAEWVYQGAIR apoB-100 4107-4121 43 1776.9092 12.946 NLQNNAEWVYQGAIR-IapoB-100 4107-4121 43 1777.9046 11.265 NLQNNAEWVYQGAIR-II apoB-1004107-4121 43 1904.9087 34.681 TPEYYPNAGLIMNYCR Lp(a) 177-192 441905.8995 35.544 TPEYYPNAGLIMNYCR-I Lp(a) 177-192 44 1906.8908 24.997SEAEDASLLSFMQGYMK apoC-III 21-37 45 1906.8908 24.997 TPEYYPNAGLIMNYCR-IILp(a) 177-192 44 1907.8826 12.840 SEAEDASLLSFMQGYMK-I apoC-III 21-37 451907.8826 12.840 TPEYYPNAGLIMNYCR-III Lp(a) 177-192 44 1922.8989 7.340SEAEDASLLSFMoxQGYMK apoC-III 21-37 45 2202.1435 41.389LREQLGPVTQEFWDNLEK apoA-I  84-101 46 2203.2007 49.396LREQLGPVTQEFWDNLEK-I apoA-I  84-101 46 2204.1703 35.316LREQLGPVTQEFWDNLEK-II apoA-I  84-101 46 2205.1404 16.202LREQLGPVTQEFWDNLEK-III apoA-I  84-101 46 2206.1991 6.574LREQLGPVTQEFWDNLEK-IIII apoA-I  84-101 46 2645.4139 −7.042VQPYLDDFQKKWQEEMELYR apoA-I 121-140 47 2646.3664 −12.328VQPYLDDFQKKWQEEMELYR-I apoA-I 121-140 47 2647.4251 −9.890VQPYLDDEQKKWQEEMELYR-II apoA-I 121-140 47 2648.3783 −5.448VQPYLDDFQKKWQEEMELYR-III apoA-I 121-140 47 2661.3337 8.767VQPYLDDFQKKWQEEMoxELYR apoA-I 121-140 47 2662.3985 12.259VQPYLDDFQKKWQEEMoxELYR-I apoA-I 121-140 47 2663.3571 9.880VQPYLDDFQKKWQEEMoxELYR-II apoA-I 121-140 47 2664.4226 5.8323VQPYLDDFQKKWQEEMoxELYR-III apoA-I 121-140 47

The m/z values are peaks that were obtained for the markers using massspectrometry system using the methods described herein.

As shown in TABLE 3, a marker may be represented at multiple m/z pointsin a spectrum. This can be due to the fact that multiple isotopes(represented in TABLE 3 as “I, II, III, IIII”) were observed, and/orthat multiple charge states of the marker were observed, or thatmultiple isoforms of the marker were observed, for example, apost-translational modification such as oxidation. These multiplerepresentations of a particular marker can be analyzed individually orgrouped together. An example of how multiple representations of a markermay be grouped is that the intensities for the multiple peaks can besummed.

As shown below in TABLE 4 and TABLE 5, targeted tandem MS analysisidentified the peptides corresponding to ten of the 13 informativefeatures shown in FIG. 4 (i.e., most significant features thatcontributed to the PLS-DA model).

TABLE 4 INFORMATIVE FEATURES REPRESENTING INCREASED PROTEIN/PEPTIDELEVELS IN CAD SUBJECTS AS COMPARED TO NORMAL SUBJECTS Magnitude Proteinin corresponding SEQ Regression to identified Protein ID Channel m/zVector peptides Residues Peptide Sequence NO 1081.6043 +22.482 apo-B100559-56 LAAYLMLMR 15 1226.547 +31.83 apo-A1 25-34 DEPPQSPWDR 25 1227.5777+22.165 apo-AI 25-34 DEPPQSPWDR-I 25 1440.6864 +48.538 Lp(a) 79-91NPDAVAAPYCYTR 35 1441.6664 +35.366 Lp(a) 79-91 NPDAVAAPYCYTR-I 351442.7047 +16.693 Lp(a) 79-91 NPDAVAAPYCYTR-II 35 1904.9087 +34.681Lp(a) 177-192 TPEYYPNAGLIMNYCR 44 1905.8995 +35.544 Lp(a) 177-192TPEYYPNAGLIMNYCR-I 44 1906.8908 +24.997 Lp(a) 177-192TPEYYPNAGLIMNYCR-II 44 1907.8826 +12.840 Lp(a) 117-192TPEYYPNAGLIMNYCR-III 44 1906.8908 +24.997 apoC-III 21-37SEAEDASLLSFMQGYMK 45 1907.8826 +12.840 apoC-III 21-37SEAEDASLLSFMQGYMK-I 45 1922.8989 +7.340 apoC-III 21-37SEAEDASLLSFMoxQGYMK 45 2202.1435 +41.39 apoA-I  84-101LREQLGPVTQEFWDNLEK 46 2203.2007 +49.39 apoA-I  84-101LREQLGPVTQEFWDNLEK-I 46 2204.1703 +35.32 apoA-I  84-101LREQLGPVTQEFWDNLEK-II 46 2205.1404 +16.202 apoA-I  84-101LREQLGPVTQEFWDNLEK-III 46 2206.1991 +6.574 apoA-I  84-101LREQLGPVTQEFWDNLEK-IIII 46 2661.3337 +8.767 apoA-I 121-140VQPYLDDFQKKWQEEM(Ox)ELYR 47 (Met112ox) 2662.3985 +12.259 apoA-I 121-140VQPYLDDFQKKWQEEM(Ox)ELYR- 47 (Met112ox) I 2663.3571 +9.880 apoA-I121-140 VQPYLDDFQKKWQEEM(Ox)ELYR- 47 (Met112ox) II 2664.4226 +5.8323apoA-I 121-140 VQPYLDDFQKKWQEEM(Ox)ELYR- 47 (met112ox) III

As shown above in TABLE 4, identification of the tryptic peptidesassociated with the positive regression vector values shown in FIG. 4revealed that, surprisingly, two peptides identified at m/z 1440 to 1442(SEQ ID NO: 35) and m/z 1904 to 1906 (SEQ ID 44) derived fromapolipoprotein(a) (Lp(a)) were increased in HDL₂ of CAD subjects, ascompared to normal subjects, FIG. 5A graphically illustrates the strongpositive informative feature in the PLS-DA regression vector at m/z1440. As shown in FIG. 5C, the positive informative feature at m/z 1440was identified by LC-MALDI-TOF/TOF MS/MS as corresponding to the peptideNPDAVAAPYCYTR (SEQ ID NO:35) which corresponds to amino acids 79-91 ofLp(a) (SEQ ID NO:4), with a MASCOT ion score of 86.46 (CI=100%). Asshown in FIG. 5B, another strong positive informative feature in thePLS-DA regression vector at m/z 1904 was identified as corresponding tothe peptide TPEYYPNAGLIMNYCR (SEQ ID NO:44), which corresponds to aminoacids 177-192 of Lp(a) (SEQ ID NO:4).

As further shown in TABLE 4, tryptic peptides identified at m/z1906-1922 (SEQ ID NO: 45) derived from apoC-III (SEQ ID NO:6) wereincreased in HDL₂ of CAD subjects, as compared to normal subjects.

TABLE 5 INFORMATIVE FEATURES REPRESENTING DECREASED PROTEIN/PEPTIDELEVELS IN CAD SUBJECTS AS COMPARED TO NORMAL SUBJECTS Magnitude Proteinin corresponding SEQ Channel Regression to Identified Protein ID m/zVector Peptides Residues Peptide sequence NO: 1012.6055 −39.93 apoA-I231-239 AKPALEDLR 11 1013.5781 −21.082 apoA-I 231-239 AKPALEDLR-I 111014.5921 −5.15 apoA-I 231-239 AKPALEDLR-II 11 1157.6638 −22.261 apoA-I202-212 LEALKENGGAR 19 1158.6367 −15.025 apoA-I 202-212 LEALKENGGAR-I 191159.6567 −5.85 apoA-I 202-212 LEALKENGGAR-II 19 1160.6312 −0.846 apoA-I202-212 LEALKENGGAR-III 19 1301.6617 −16.664 apoA-I 185-195 THLAPYSDELR27 1302.6514 −82.138 apoA-I 185-195 THLAPYSDELR-I 27 1303.6417 −50.00apoA-I 185-195 THLAPYSDELR-II 27 1302.6514 −82.138 apoA-I 165-175LSPLGEEMRDR 28 1303.6417 −50.00 apoA-I 165-175 LSPLGEEMRDR-I 281380.7137 −20.69 apoA-I 121-131 VQPYLDDFQKK 30 1381.7081 −15.84 apoA-I121-131 VQPYLDDFQKK-I 30 1488.7235 −10.607 apoC-I 64-74 MREWFSETFQK 361489.7361 −8.862 apoC-I 64-74 MREWFSETFQK-I 36 1490.6898 −3.125 apoC-I64-74 MREWFSETFQK-II 36 1504.7079 −12.493 apoC-I 64-74 MoxREWFSEFQK 361505.7314 −12.066 apoC-I 64-74 MoxREWFSETFQK-I 36 1506.6954 −5.215apoC-I 64-74 MoxREWFSETFQK-II 36 1612.7768 −17.53 apoA-I 70-83LLDNWDSVTSTFSK 39 2645.4139 −7.042 apoA-I 121-140 VQPYLDDFQKKWQEEMELYR47 2646.3664 −12.328 apoA-I 121-140 VQPYLDDFQKKWQEEMELYR-I 47 2647.4251−9.89 apoA-I 121-140 VQPYLDDFQKKWQREMELYR-II 47 2648.3783 −5.448 apoA-I121-140 VQPYLDDFQKKWQEEMELYR-III 47

As shown above in TABLE 5, identification of the tryptic peptidesassociated with the negative regression vector values shown in FIG. 4revealed several peptides from apoA-I and a peptide from apoC-I weredecreased in HDL₂ of CAD subjects compared to that of control subjects.The peptides derived from apoA-I (SEQ ID NO:1) that were identified asdecreased in CAD subjects included SEQ ID NO: 11, SEQ ID NO:19, SEQ IDNO:27, SEQ ID NO:28, SEQ ID NO:30, SEQ ID NO:39 and SEQ ID NO:47, asshown in TABLE 5.

The negative regression vector at m/z 1504-1506 was identified ascorresponding to the peptide MREWFSETFQK (SEQ ID NO: 36) whichcorresponds to amino acids 64-74 of ApoC-I (SEQ ID NO: 5).

Taken together, these results demonstrate that pattern recognitionprofiling performed on HDL₂ isolated from CAD and control subjects showaltered patterns of apoproteins present in the HDL₂ fractions which fallinto two classes: (1) increased levels of peptides/proteins in CADsubjects as compared to normal controls; or (2) decreased levels ofpeptides/proteins in CAD subjects as compared to normal controls.

The observation that levels of Lp(a) were found to be increased in CADsubjects in comparison to normal controls was a surprising resultbecause Lp(a) has been shown to be associated with small dense lowdensity lipoproteins (LDLs), and its association with HDLs in general,and HDL₂ in particular, has not been previously shown. Thus, theseresults demonstrate that co-isolation of Lp(a) with HDL₂ subfractionpermits pattern recognition analysis of the subfraction in theprediction, diagnosis, and prognosis of CAD subjects.

It was also observed that levels of apoC-III peptides were found to beelevated in CAD subjects, whereas those of apoC-I were decreased. Inthis regard, although not wishing to be bound by theory, it is notedthat apoC-III inhibits lipoprotein lipase and the hepatic uptake oftriglyceride-rich lipoproteins, which might promote an increase inatherogenic triglyceride-rich lipoproteins (see Ooi, E. M., et al.,Clin. Sci. (Lond.) 114:611-624, 2008. It is further noted that ApoC-Iinhibits cholesterol ester transfer protein (CETP) (see Shachter, N. S.,et al., Curr. Opin. Lipidol. 12:297-304, 2001; Sparks, D. I., et al., J.Lipid Res. 30:1491-1498, 1989. Thus, it is believed that alterations inapoC-I and apoC-III levels are likely contribute to lipid remodeling andthe formation of pro-atherogenic HDL particles.

Therefore it is demonstrated that simultaneous profiling of thesebiomarkers in subjects using pattern recognition analysis may be used toaid in the diagnosis and prognosis of cardiovascular diseases inmammalian subjects.

Example 4

This example demonstrates that subjects may be successfully classifiedas CAD or control subjects based on the oxidation status of their HDL₂using PLS-DA based pattern recognition proteomic profiling.

Methods:

Sample Preparation and Analysis:

HDL₂ fractions were isolated from subjects, and samples from eachindividual subject were subjected to MALDI-TOF/TOF MS and PLS-DAanalyses, as described in Example 2. Subjects were classified as eitherCAD or normal control subjects by pattern recognition proteomicprofiling of HDL₂ proteins using PLS-DA. The PLS-DA models werecharacterized by regression vectors as described in Example 3. ThePLS-DA model regression vector analysis is centered onpost-translationally modified peptides derived from apoA-I, the majorprotein in HDL₂.

Results:

In addition to the first two groups of informative features (increasedor decreased peptide levels in CAD subjects as compared to normalsubjects) as described in Example 2, a third group of informativefeatures in the PLS-DA model was identified that centered onpost-translationally modified peptides derived from apoA-I (SEQ EDNO:1), the major protein in HDL. MS/MS analysis confirmed the presenceof these peptide sequences in the HDL₂ fraction and demonstrated thatthe methionine 112 residue had been converted to methionine sulfoxide(Met(O)).

As shown in FIGS. 6A-D and summarized below in TABLE 6, this third groupof informative features included both native peptides KWQEEMELYR (SEQ IDNO:33) and VQPYLDDFQKKWQEEMELYR (SEQ ID NO: 47) and the correspondingoxidized peptides that contained methionine 112 (Met112). FIG. 6Agraphically illustrates the negative regression vector at m/z 1411.7077and the positive regression vector at m/z 1427.6644 which wereidentified as corresponding to the native form of the apoA-I peptideKWQEEMELYR (SEQ ID NO: 33), and the Met112 oxidized form KWQEEM(O)ELYRof SEQ ID NO:33, respectively, as shown in FIG. 6C (MASCOT ion score of84.8, CI=100%).

FIG. 6B graphically illustrates the negative regression vector at m/z2646.3664 and the positive regression vector at m/z 2662.3985 which wereidentified as corresponding to the native form of the apoA-I peptideVQPYLDDFQKKWQEEMELYR (SEQ ID NO:47), and the Met112 oxidized formVQPYLDDFQKKWQEEM(O)ELYR of SEQ ID NO:47, respectively, as shown in FIG.6D (MASCOT ion score of 42.6, CI=99.96%).

TABLE 6 INFORMATIVE FEATURES REPRESENTING POSTTRANSLATIONALLY MODIFIEDPEPTIDES IN CAD SUBJECTS AS COMPARED TO NORMAL SUBJECTS Magnitude inProtein/ Channel Regression peptide SEQ m/z Vector location ModificationPeptide Sequence ID 1411.7077 −12.606 apoA-I native KWQEEMELYR 33(131-140) 1427.6644 +11.458 apoA-I oxidized M112 KWQEEM(O)ELYR 33(131-140) (MetOx) 2646.3664 −12.328 apoA-I native VQPYLDDFQKKWQEEMELYR-I47 (121-140) 2662.3985 +12.259 apoA-I oxidized M112VQPYLDDFQKKWQEEM(O)ELYR-I 47 (121-140) (MetOx)

Strikingly, as shown in FIG. 6 and summarized above in TABLE 6, thesignals for the Met 112 oxidized (Met112(O)) apoA-I peptides (SEQ IDNO:33 and SEQ ID NO:47) were found to be increased in CAD subjects ascompared to normal control subjects, while the levels of thecorresponding native Met112 peptides (SEQ ID NO:33 and SEQ ID NO:47)were found to be decreased in CAD subjects as compared to normal controlsubjects.

It is noted however, that no difference in relative levels of othermethionine containing native and oxidized peptides, such as thosederived from apoC-I, were observed between normal controls and CADsubjects was observed in this analysis (data not shown), suggesting thatthe difference in levels of oxygenated Met112 did not result from exvivo oxidation.

While not wishing to be bound by theory, oxidation has been proposed asone mechanism for generating dysfunctional HDL resulting in decreasedreverse cholesterol transport, thereby disrupting normal cholesterolhomeostasis. Lipid hydroperoxides and reactive intermediates produced byMyeloperoxidase (MPG) oxidize apoA-I. It has been shown that oxidationof methionine residues impairs apoA-I's ability to promote cholesterolefflux by the ABCA1 pathway (Shao, B., et al. J. Biol. Chem.281(14):9001-9004 (2006) and to activate LCAT, two key steps in removingcholesterol from lipid-laden macrophages. apoA-I co-localizes with HOCloxidation adducts in human atherosclerotic tissues. MPO-produced HOCl isknown to modify HDL in vivo. Antibodies specific for apoA-I andHOCl-modified proteins immunostained coronary arteries obtained frompatients undergoing cardiac transplantation (O'Brien et al., Circulation98:519-527, 1998). ApoA-I co-localized with epitopes recognized by HOP-Iantibody, which is specific for proteins oxidized by HOCl (Hazell etal., J. Clin. Invest. 97:1535-1544, 1996) in the intima ofatherosclerotic lesions. The co-localization of HOCl-modified proteinswith apoA-I suggests that HOCl oxidizes specific proteins in the humanartery wall.

Oxidized HDLs are also present in the circulation of CVD patients,(International Publication No. WO2006/014628). Circulating HDL fromcardiovascular patients has 8-times higher 3-chlorotyrosine than normalsubjects. Levels of chlorinated HDL are elevated in the blood of humanssuffering from clinically significant atherosclerosis. In addition,MPO-produced H₂O₂ is also capable of oxidizing methionines of apoA-Iassociated with HDL₃ (International Publication No. WO2006/014628).These HDL₃ subfractions are selectively enriched with oxidized aminoacids.

Collectively, these observations support the conclusion that HDL₂ fromcontrol and CAD subjects differ in their protein cargoes and levels ofoxidized methionine residues. Because pattern recognition analysis makesno assumptions about the origins of the differential signals seen in theregression vectors for each sample, it provides a powerful tool foridentifying post-translationally modified peptides that would be verydifficult to identify using classic proteomic approaches. The resultsdemonstrated in this example indicate that oxidized methionines (Met(O))in apoA-I are detectable by pattern recognition profiling of an HDL₂subfraction. Since oxidation of methionine residues impairs apoA-I'sability to promote cholesterol efflux by the ABCA1 pathway (Shao, B., etal., “Myeloperoxidase impairs ABCA1-dependent cholesterol efflux throughmethionine oxidation and site-specific tyrosine chlorination ofapolipoprotein A-I,” J. Biol. Chem. 281:9001-4, 2006) and to activateLecithin:Cholesterol Acyltransferase (LCAT) (Shao, B., et al.,“Methionine Oxidation Impairs Reverse Cholesterol Transport byApolipoprotein A-I,” Proc. Natl. Acad. Sci. 105(34):12224-12229, Aug.26, 2008), oxidized apoA-I likely acts as a mediator of CAD, and servesas a useful biomarker for CAD. Thus, a subject may be evaluated fir thepresence of oxidized apoA-I (SEQ ID NO:1) to determine the risk,diagnosis, prognosis of CAD in the subject and/or to measure theefficacy of treatment of a subject suffering from CAD.

Example 5

This example demonstrates that the conformational structure of apoA-I inHDL₂ subfractions is altered in CAD subjects as compared to theconformation structure of apoA-I in HDL₂ subfractions of normal controlsubjects.

Rationale:

The structural conformation of apoA-I has been suggested to influenceits ability to transfer cholesterol ester from HDL₂ particles toscavenger receptor BI as part of reverse cholesterol transport andcholesterol ester clearance in the liver (de Beer, M. C., et al., J.Lipid Res. 42:309-313, February 2001). Contact between the N-terminalfold and the C-terminal domain of apoA-I has been suggested to stabilizethe lipid-bound conformation of the protein. Since methionines in apoA-Iare oxidized in CAD subjects, as demonstrated in Example 4, anexperiment was carried out to determine if such post-translationalmodifications lead to local changes in the structural conformation ofapoA-I. Alterations in a protein's local structure is said to affectsusceptibility of the protein to proteolytic digestion, which in turncan affect the apparent abundance of peptides assessed by MS.

Methods:

Sample Preparation and Analysis:

HDL₂ fractions were isolated from subjects and treated with trypsin asdisclosed in Example 1. The samples from each individual subject weresubjected to MALDI-TOF/TOF MS, as described in Example 2. Subjects wereclassified as either CAD or normal subjects by pattern recognitionproteomic profiling of HDL₂ proteins using PLS-DA. The PLS-DA modelswere characterized by regression vectors as described in Example 3. ThePLS-DA model regression vector analysis is centered on apoA-I peptidesof HDL₂. The differential signals reflecting the relative abundance ofthe trypsinized peptides was measured.

Results:

In addition to the three groups of informative features (increased ordecreased peptide levels in CAD subjects as compared to normal subjects)as described in Example 2, and post-translationally modified peptidesderived from apoA-I as described in Example 3, a fourth group ofinformative features in the PLS-DA model was identified based on thealtered structural conformation of apoA-I present in the HDL₂subfraction of CAD subjects in comparison to the structural conformationof apoA-I present in the HDL₂ subjection of normal subjects.

Informative features corresponding to tryptic peptides derived from theN-terminal and C-terminal regions of apoA-I (SEQ ID NO:1) wereidentified. FIG. 7 graphically illustrates the regression vector values(y-axis) for the amino acid sequence of apoA-I (x-axis).

It was determined that two tryptic apoA-I peptides originating fromN-terminal regions of the mature protein (residues 1-10: DEPPQSPWDR (SEQID NO:48) and residues 60-77, LREQLGPVTQEFWDNLEK (SEQ ID NO:49) weresignificantly increased in CAD subjects as compared to normal controls,while one C-terminal region peptide (residues 207-215, AKPALEDLR (SEQ IDNO:50) was significantly decreased as compared to normal controls, asshown in FIG. 7. Also, a tryptic peptide (peptide 46-59: LLDNWDSVTSTFSK(SEQ ID NO:52) was apparently decreased in abundance. These observationssuggest that tryptic digests of apoA-I in HDL isolated from control andCAD subjects give different patterns of peptides, perhaps because ofconformational differences of the apoA-I in the two different classes ofsubjects. Indeed, although the above-referenced N-terminal peptides (SEQID NO:48 and SEQ ID NO:49) and C-terminal peptides (SEQ ID NO:49) aredistant in apoA-I sequence, when mapped to the double-belt model of thelipid-associated apoA-I (Davidson, W. S., et al., J. Biol. Chem.282(31)22249-22253, 2007) or spherical HDL particle apoA-I model(Gangani, R. A., et al., Proc. Natl. Acad. Sci. 105(34):12176-12181,Aug. 26, 2008), the peptides displaying significant changes in CADsubjects were found to be in close proximity (data not shown).

Additionally, it was determined that the peptides (residues 97-107,VQPYLDDFQKK SEQ ID NO: 51) proximal to Met112 was significantlydecreased in the CAD samples (FIG. 7), as is the peptide containingMet112, SEQ ID NO:33.

It was recently proposed that contact between the globular N-terminalfold and the C-terminal fold of apoA-I stabilizes the lipid-boundconformation of the protein. It is important to note that alterations ina proteins local structure can effect susceptibility to proteolyticdigestion, which in turn can affect the apparent abundance of peptidesin a MS analysis. As demonstrated in this example, the differentiallevels of N-terminal and C-terminal apoA-I peptides indicates that thesecondary and/or tertiary conformations at the N and C-termini of apoA-Idiffer in the HDL₂ of CAD subjects as compared to normal controlsubjects. Further in this regard, as described in Example 4 andsummarized in TABLE 6, it was also determined that levels of apoA-Ipeptides containing Met(O)112 were elevated in the HDL₂ of CAD subjectsconcomitantly with a decrease in Met112 peptides in the CAD subjects.The peptides directly adjacent to the peptides containing Met112 alsodisplayed significant changes in CAD subjects as compared to normalcontrols. While not wishing to be bound by theory, these observationssuggest that oxidation of methionine residues in apoA-I is increased inCAD subjects and such oxidation may lead to local changes in theconformation of the apoA-I protein which can be detected by trypticdigestion followed by analysis by mass spectrometry.

The results described in this example demonstrate that the alteredconformation of apoA-I at its N- and C-termini is detectable usingPLS-DA-based pattern recognition profiling. Changes in relativeabundance of certain tryptic peptides demonstrate that apoA-I exists inaltered secondary and/or tertiary conformation in HDL₂ subfractions ofCAD subjects compared to control subjects. Thus, dysfunctionality ofHDL₂ of CAD subjects likely results from changes in the proteome profileand conformation of the associated HDL₂ proteins. These resultsdemonstrate that pattern recognition profiling using tryptic peptides ofHDL₂ subfractions from subjects can be used to determine theconformation status of apoA-I in order to classify subjects as normal orCAD patients.

While the preferred embodiment of the invention has been illustrated anddescribed, it will be appreciated that various changes can be madetherein without departing from the spirit and scope of the invention.

The invention claimed is:
 1. A method for determining the efficacy of a treatment regimen for treating and/or preventing cardiovascular disease in a subject, the method comprising monitoring a measurable feature of at least two biomarkers selected from the group consisting of apoA-I, apoA-II, apoB-100, Lp(a), apoC-I, and apoC-III, combinations or portions and/or derivatives thereof in an HDL subfraction or in a complex containing apoA-I or apoA-II isolated from a biological sample obtained from the subject during treatment for cardiovascular disease.
 2. The method of claim 1, wherein the monitoring comprises detecting the measurable feature of the at least two biomarkers in biological samples obtained at a one or more time points during the treatment for cardiovascular disease.
 3. The method of claim 2, further comprising comparing the measurable features of the at least two biomarkers as detected in biological samples obtained at two or more time points during the treatment for cardiovascular disease.
 4. The method of claim 3, wherein a difference in the measurable features of the at least two biomarkers from biological samples obtained from the subject at the two or more time points during treatment is indicative of the efficacy of the treatment regimen for treating and/or preventing cardiovascular disease in the subject.
 5. The method of claim 4, wherein at least one of the measurable features indicative of the efficacy of the treatment regimen for treating and/or preventing cardiovascular disease comprises an increased amount of at least one of the biomarkers in the HDL subfraction or in the complex containing apoA-I or apoA-II isolated from the biological sample selected from the group consisting of apoA-I, apoB-100, apoC-III and Lp(a), or portions and/or derivatives thereof, in comparison to the amount of the at least one of the biomarkers in the HDL subfraction or in the complex containing apoA-I or apoA-II determined in a biological sample obtained at a later time point.
 6. The method of claim 5, wherein the biomarker is apoA-I, or a portion or derivative thereof.
 7. The method of claim 5, wherein the biomarker is apoC-III or a portion or derivative thereof.
 8. The method of claim 5, wherein the biomarker is Lp(a) or a portion or derivative thereof.
 9. The method of claim 4, wherein at least one of the measurable features indicative of the efficacy of the treatment regimen for treating and/or preventing cardiovascular disease comprises a decreased amount of at least one of the biomarkers in the HDL subfraction or in the complex containing apoA-I or apoA-II isolated from the biological sample selected from the group consisting of apoA-I and apoC-I, or portions and/or derivatives thereof, in comparison to the amount of the at least one of the biomarkers in the HDL subfraction or in the complex containing apoA-I or apoA-II determined in a biological sample obtained at a later time point.
 10. The method of claim 9, wherein the biomarker is apoA-I, or a portion or derivative thereof.
 11. The method of claim 9, wherein the biomarker is apoC-I, or a portion or derivative thereof.
 12. The method of claim 4, wherein at least one of the measurable features indicative of the efficacy of the treatment regimen for treating and/or preventing cardiovascular disease comprises an increased or decreased presence or amount of a post-translational modification of a peptide derived from apoA-I in the HDL subfraction or complex isolated from the biological sample, in comparison to the presence or amount of the post-translational modification of the at least one of the biomarkers in the HDL subfraction or in the complex determined in a biological sample obtained at a later time point.
 13. The method of claim 12, wherein the post-translational modification of apoA-I is oxidation of at least one Methionine residue.
 14. The method of claim 4, wherein at least one of the measurable features indicative of the efficacy of the treatment regimen for treating and/or preventing cardiovascular disease comprises an altered structural conformation of apoA-I in the HDL subfraction of the biological sample, in comparison to the structural conformation of apoA-I in the HDL subfraction or in the complex determined in a biological sample obtained at a later time point.
 15. The method of claim 4, wherein the measurable features of the at least two biomarkers from the biological samples are determined using mass spectrometry analysis.
 16. The method of claim 15, wherein the mass spectrometry analysis is performed on a tryptic digestion of the HDL subfraction or complex isolated from the biological sample.
 17. The method of claim 15, wherein the mass spectrometry analysis is carried out with a matrix-assisted laser desorption ionization (MALDI) mass spectrometer or LCMS.
 18. The method of claim 1, wherein the HDL subfraction of the biological sample is the HDL2 subfraction.
 19. The method of claim 1, wherein the biological sample is selected from the group consisting of a blood sample, a serum sample, a plasma sample, a tissue sample, a bodily fluid sample, and a urine sample.
 20. The method of claim 1, wherein the cardiovascular disease is the predisposition to myocardial infarction, atherosclerosis, coronary artery disease, peripheral artery disease, heart failure, or stroke.
 21. The method of claim 1, wherein the measurable features of the at least two biomarkers in the HDL subfraction or complex isolated from the biological sample are detected using at least one antibody specific to each of the at least one of the two biomarkers. 